基于粗糙集的过程建模、控制与故障诊断
摘要
传统的工业过程建模、控制及故障诊断技术大多基于精确的数学模型，适用于已知运行机理和过程特性的工业系统。然而，工业系统的结构、参数和运行模式在不断的变化，对过程机理的透彻分析和精确数学模型的建立常常需要付出无法接受的代价，系统的控制和故障诊断也就因此面临一个精确建模问题。在实际工业生产中，工程技术人员在对系统机理和数学模型知之甚少的情况下通过观察和经验总结，仍然能够对系统进行良好的手动控制和及时的故障诊断，因此完全可以采用机器学习的智能方法模拟人的这种通过观察学习进行控制的能力。
粗糙集理论是一种通过等价关系和近似概念对数据进行约简以获取知识的方法。粗糙集信息系统是一个基于规则的知识系统，一个基于规则的系统不需要对过程进行数学描述，而是对过程进行经验总结，形式上是“如果…那么…”的规则的集合。规则系统提供了一种了解工业过程的更加直观、简单、易于理解和人性化、智能化的方法。粗糙集理论为基于知识的过程建模、控制与故障诊断提供了理论基础和研究思路。本文的主要目的是试图解决粗糙集在知识获取、机器学习以及工业生产实际运用中所遇到一些主要问题，利用粗糙集在分析处理不完全、不精确和不一致数据中所具有的优势，对复杂工业过程进行基于规则的非机理知识建模，在规则模型的基础上，结合现代控制理论中的有关概念和方法，构造粗糙控制的初步框架，并将粗糙集方法运用于故障诊断。
本文的主要贡献如下：
1、回顾和总结了粗糙集理论的发展和国内外的研究现状，讨论了其在复杂工业过程建模、控制与故障诊断中应用的一般方法。对于粗糙集的开发工具、研究及应用成果以及在工业控制中的发展过程作了系统深入的综述，重点分析和评述了粗糙集理论在工业过程建模、控制与故障诊断中所面临的问题。
2、介绍了粗糙集信息系统中数据预处理的几种方法，分析了连续属性离散化对知识获取的影响。基于布尔逻辑与粗糙集相结合的离散化方法，提出了连续／离散属性混合系统离散化的策略和启发式算法，在信息熵的基础上，提出了一种对离散化结果进行评判的标准，并对布尔逻辑离散化方法和混合离散化算法进行了评价。
3、在等价矩阵概念的基础上，分析了粗糙集知识系统中等价划分与等价
摘要
矩阵的关系，采用等价矩阵来表示粗糙集的等价关系，提出了一种对
数据库知识系统进行数据清洗以及从中提取决策规则的矩阵算法，分
析了该算法的计算复杂性。基于已有的决策规则根据对象增减，提出
了在粗糙集信息系统中对决策规则进行动态调整的新策略，并进一步
给出了实现该策略的改进矩阵算法。
4、从非线性系统分析方法出发，构造了基于粗糙集决策规则的非线性算
子方程和粗糙状态空间，分析了粗糙状态空间模型的一致性和完备性，
给出了对不一致和不完备的粗糙状态空间模型进行补充的方法。初步
讨论了粗糙状态空间模型的稳定性和可达性，提出了基于粗糙状态空
间模型的非线性过程建模和粗糙控制器设计方法。以一个具有非线性
特性的自来水加药混凝沉淀系统为背景进行了仿真研究，结果表明了
基于粗糙集决策规则的粗糙状态空间模型和粗糙控制器在非线性过程
建模与控制中的有效性。
5、利用粗糙集理论自动获取过程工业生产系统中的故障知识，从信息嫡
的角度分析系统知识不确定性的变化，提出了一种基于粗糙集理论的
故障诊断新方法，在前向推理、反向推理以及混合推理的基础上，给
出了针对故障点建立决策表以及利用粗糙集约简所获得的诊断规则进
行正、反向故障诊断的步骤，讨论了这种故障诊断方法的诊断性能及
其在计算上的复杂度。利用粗糙集进行故障知识的发现，寻找系统各
个故障源信号之间合理的逻辑关系，提出了一种构造逻辑故障树的智
能方法，并给出了相应的故障树评价标准。以双效蒸发器系统为背景，
进行了基于粗糙集的故障诊断实例仿真。
6、对下一步在基于粗糙集的过程建模、控制和故障诊断等方面将要进行
的工作进行了展望。
The traditional approaches of industrial modeling, control and fault diagnosis are mainly based on analytical models. Those models are suitable for system that we known clearly how they work. When facing to complex, dynamic and nonlinear systems, it is usually hard to analysis the mechanism of processes and to build up mathematic models, or it will take us unbearable costs. In most situations a nonlinear system has special properties that are different from the others, so we can't find out just one or few methods suitable to model and control all those nonlinear systems. An experienced human operator may have little knowledge about a complex system but can still doing good job in system control and fault diagnosis by observing signals of inputs and outputs. Therefore, the problem is, can we use some techniques of machine learning and artificial intelligent to mimic the human ability of "learn to control".
Rough set theory is a new mathematical and AI technique in knowledge discovery. Rough set information system is a rule based knowledge system. A rule based system does not require a classical mathematical description of the process, but consist of sets of If....Then.... rules instead. By rule based system we can understand industrial processes in a visualized, simple, understandable, humanistic and intelligent way. The aims of this dissertation include: try to solve problems in rough set based knowledge discovery and machine learning; build up knowledge model for complex industrial processes; following the concepts and approaches of nonlinear system control, construct a control system framework based on rough state space; apply rough set theory to fault detection and diagnosis (FDD).
The main contributions of the dissertation are as follows:
1 Reviewed the developments and research situation of rough set theory. Discussed the common approaches in industrial modeling, control and FDD. Investigations about implemental system and achievements in industrial applications of rough set theory have been done. The problems of rough set theory in industrial modeling control and FDD are pointed .
2, To be an improvement of discretization method based on rough set theory and Boolean reasoning, algorithms for discretization in mixed real
and discrete attributes system are proposed, functions based on information entropy are presented to analysis the results of discretization quantitatively, and the effects of discretization for knowledge acquisition are discussed.
3 The concept of equivalence matrix, which expresses equivalence relation in rough set information system, is introduced; the relations between equivalence matrix and equivalence classes are discussed. The algorithms for data cleaning and rules extraction in knowledge system based on matrix computation are proposed and their complexity of computation is analyzed. For online learning of a rule-based knowledge system, strategies of rule updating in rough set information system and matrix computation algorithms for dynamic modification of rules are given.
4 Based on definitions of nonlinear system methodology, rough set based state space model is proposed. Definitions of rough set state space are given. Stability, reachability, consistency and completeness of this new state space model are discussed in detail, respectively. A rule-based structure of Internal Model Control (IMC) system is given, and a rule-based IMC controller is built up.
5 Approach for rough set based FDD is proposed, and its applicability and computational complexity is discussed. Knowledge about the faults of a process control system is obtained automatically by rough set reduction. An entropy-based criterion is used to measure the uncertainty of it. Methods of forward and backward fault diagnosis and how to build up decision tables for each fault source are given. Further more, approach for FDD based on rough set theory and Logical Fault Tree ( LFT) is presented. The feasibility of forming a knowledge discovering and intelligent decision-making system for these a
粗糙集理论是一种通过等价关系和近似概念对数据进行约简以获取知识的方法。粗糙集信息系统是一个基于规则的知识系统，一个基于规则的系统不需要对过程进行数学描述，而是对过程进行经验总结，形式上是“如果…那么…”的规则的集合。规则系统提供了一种了解工业过程的更加直观、简单、易于理解和人性化、智能化的方法。粗糙集理论为基于知识的过程建模、控制与故障诊断提供了理论基础和研究思路。本文的主要目的是试图解决粗糙集在知识获取、机器学习以及工业生产实际运用中所遇到一些主要问题，利用粗糙集在分析处理不完全、不精确和不一致数据中所具有的优势，对复杂工业过程进行基于规则的非机理知识建模，在规则模型的基础上，结合现代控制理论中的有关概念和方法，构造粗糙控制的初步框架，并将粗糙集方法运用于故障诊断。
本文的主要贡献如下：
1、回顾和总结了粗糙集理论的发展和国内外的研究现状，讨论了其在复杂工业过程建模、控制与故障诊断中应用的一般方法。对于粗糙集的开发工具、研究及应用成果以及在工业控制中的发展过程作了系统深入的综述，重点分析和评述了粗糙集理论在工业过程建模、控制与故障诊断中所面临的问题。
2、介绍了粗糙集信息系统中数据预处理的几种方法，分析了连续属性离散化对知识获取的影响。基于布尔逻辑与粗糙集相结合的离散化方法，提出了连续／离散属性混合系统离散化的策略和启发式算法，在信息熵的基础上，提出了一种对离散化结果进行评判的标准，并对布尔逻辑离散化方法和混合离散化算法进行了评价。
3、在等价矩阵概念的基础上，分析了粗糙集知识系统中等价划分与等价
摘要
矩阵的关系，采用等价矩阵来表示粗糙集的等价关系，提出了一种对
数据库知识系统进行数据清洗以及从中提取决策规则的矩阵算法，分
析了该算法的计算复杂性。基于已有的决策规则根据对象增减，提出
了在粗糙集信息系统中对决策规则进行动态调整的新策略，并进一步
给出了实现该策略的改进矩阵算法。
4、从非线性系统分析方法出发，构造了基于粗糙集决策规则的非线性算
子方程和粗糙状态空间，分析了粗糙状态空间模型的一致性和完备性，
给出了对不一致和不完备的粗糙状态空间模型进行补充的方法。初步
讨论了粗糙状态空间模型的稳定性和可达性，提出了基于粗糙状态空
间模型的非线性过程建模和粗糙控制器设计方法。以一个具有非线性
特性的自来水加药混凝沉淀系统为背景进行了仿真研究，结果表明了
基于粗糙集决策规则的粗糙状态空间模型和粗糙控制器在非线性过程
建模与控制中的有效性。
5、利用粗糙集理论自动获取过程工业生产系统中的故障知识，从信息嫡
的角度分析系统知识不确定性的变化，提出了一种基于粗糙集理论的
故障诊断新方法，在前向推理、反向推理以及混合推理的基础上，给
出了针对故障点建立决策表以及利用粗糙集约简所获得的诊断规则进
行正、反向故障诊断的步骤，讨论了这种故障诊断方法的诊断性能及
其在计算上的复杂度。利用粗糙集进行故障知识的发现，寻找系统各
个故障源信号之间合理的逻辑关系，提出了一种构造逻辑故障树的智
能方法，并给出了相应的故障树评价标准。以双效蒸发器系统为背景，
进行了基于粗糙集的故障诊断实例仿真。
6、对下一步在基于粗糙集的过程建模、控制和故障诊断等方面将要进行
的工作进行了展望。
The traditional approaches of industrial modeling, control and fault diagnosis are mainly based on analytical models. Those models are suitable for system that we known clearly how they work. When facing to complex, dynamic and nonlinear systems, it is usually hard to analysis the mechanism of processes and to build up mathematic models, or it will take us unbearable costs. In most situations a nonlinear system has special properties that are different from the others, so we can't find out just one or few methods suitable to model and control all those nonlinear systems. An experienced human operator may have little knowledge about a complex system but can still doing good job in system control and fault diagnosis by observing signals of inputs and outputs. Therefore, the problem is, can we use some techniques of machine learning and artificial intelligent to mimic the human ability of "learn to control".
Rough set theory is a new mathematical and AI technique in knowledge discovery. Rough set information system is a rule based knowledge system. A rule based system does not require a classical mathematical description of the process, but consist of sets of If....Then.... rules instead. By rule based system we can understand industrial processes in a visualized, simple, understandable, humanistic and intelligent way. The aims of this dissertation include: try to solve problems in rough set based knowledge discovery and machine learning; build up knowledge model for complex industrial processes; following the concepts and approaches of nonlinear system control, construct a control system framework based on rough state space; apply rough set theory to fault detection and diagnosis (FDD).
The main contributions of the dissertation are as follows:
1 Reviewed the developments and research situation of rough set theory. Discussed the common approaches in industrial modeling, control and FDD. Investigations about implemental system and achievements in industrial applications of rough set theory have been done. The problems of rough set theory in industrial modeling control and FDD are pointed .
2, To be an improvement of discretization method based on rough set theory and Boolean reasoning, algorithms for discretization in mixed real
and discrete attributes system are proposed, functions based on information entropy are presented to analysis the results of discretization quantitatively, and the effects of discretization for knowledge acquisition are discussed.
3 The concept of equivalence matrix, which expresses equivalence relation in rough set information system, is introduced; the relations between equivalence matrix and equivalence classes are discussed. The algorithms for data cleaning and rules extraction in knowledge system based on matrix computation are proposed and their complexity of computation is analyzed. For online learning of a rule-based knowledge system, strategies of rule updating in rough set information system and matrix computation algorithms for dynamic modification of rules are given.
4 Based on definitions of nonlinear system methodology, rough set based state space model is proposed. Definitions of rough set state space are given. Stability, reachability, consistency and completeness of this new state space model are discussed in detail, respectively. A rule-based structure of Internal Model Control (IMC) system is given, and a rule-based IMC controller is built up.
5 Approach for rough set based FDD is proposed, and its applicability and computational complexity is discussed. Knowledge about the faults of a process control system is obtained automatically by rough set reduction. An entropy-based criterion is used to measure the uncertainty of it. Methods of forward and backward fault diagnosis and how to build up decision tables for each fault source are given. Further more, approach for FDD based on rough set theory and Logical Fault Tree ( LFT) is presented. The feasibility of forming a knowledge discovering and intelligent decision-making system for these a
引文
Ahn B.S, Cho S.S, Kim C.Y, The integrated methodology of rough set theory and artificial neural network for business failure prediction, Expert Systems with Applications, 2000.2, 18 (2): 65-74
Aleksander hrn, Rough logic control: A new approach to automatic control? Knowledge systems group, Fculty of Electrical Engineering and Computer Science, Norwegian Institute of Technology, University of Trondheim,Trondheim, Norway, 1993, 4, 29
Alonge F, D'Ippolito F, Pizzuto G, et al, Identification of nonlinear vectorial functions by means of rough set and fuzzy logic theories, Proceedings of the 1998 IEEE International Conference on Control Applications, 1998, 2:942-947
Amin Mousavi, Parviz Jabedar-Maralani, Double-faced rough sets and rough communication, Information Sciences, 2002, 148:41-53
An A, Chan C, Shan N, et al, Applying knowledge discovery to predict water-supply consumption, IEEE Expert [see also IEEE Intelligent Systems], 1997.7,12 (4): 72-78
Anantaram C, Nagaraja G, Non K.V, Verification of accuracy of rules in a rule based system Data & Knowledge Engineering, 1998, 27:115-138
Anderson K.L, Blankenship G.L, Lebow L.G, A rule-based adaptive PID controller, Proceedings of the 27th IEEE Conference on Decision and Control, 1988.12.7-9, 1:564-569
Andrzej Czyzewski, Automatic identication of sound source position employing neural networks and rough sets, Pattern Recognition Letters, 2003, 24:921-933
Andrzej Skowron, Zbigniew Suraj, A parallel algorithm for real-time decision making: A rough set apporach, Journal of Intelligent Information Systems (JIIS), 1996.9, 7 (1): 5-28
Arima M, Hara E.H, Katzberg J.D, A fuzzy logic and rough sets controller for HVAC systems, WESCANEX 95. Communications, Power, and Computing, Conference Proceedings. IEEE , 1995.5, 1: 133-138
Arkadiusz Wojna, Constraint based incremental learning of classification rules,
Lecture Notes in Artificial Intellingence, 2000.1, pp: 0428,
Bakar A.A, Sulaiman M.N, Othman M, et al, Finding minimal reduct with binary integer programmingindatamining, TENCON2000, Proceedings, 2000, 2:141-146
Batur C, Srinivasan A, Chan C.C, Automated rule based model generation for uncertain complex dynamic systems, Int. J. Of Engineering Applications of Artificial Intelligence, 1991, 4 (5): 359-366
Bautista R, Millan M, Diaz J.F, An efficient implementation to calculate relative core and reducts, Fuzzy Information Processing Society, NAFIPS. 18th International Conference of the North American , 1999.7, pp: 791-794
Bazan J, Skowron A, Synak P, Dynamic reducts as a tool for extracting laws from decision tables, In Proc: the Syrup. Methodologies for Intelligent Systems, Charlotte, NC, Lecture Notes in Artificial Intelligence, Berlin, pringerVerlag, 1994, pp: 346-355.
Beaubouef T, Petry F.E, A rough set foundation for spatial data mining involving vague regions, FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, 2002, 1: 767-772
Beaubouef T, Petry F, Vague regions and spatial relationships" A rough set approach, Fourth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'01 ), YokosukaCity, Japan, 2001.10.30-11.1, pp: 313-318
Beniaminy I, Kanonicz G, Neuhaus R, Creating an advanced diagnostic knowledge base from existing fault trees: a case study, AUTOTESTCON'99. IEEE Systems Readiness Technology Conference, 1999, pp: 153-159
Beynon Malcolm J, Peel Michael J, Variable precision rough set theory and data discretisation: an application to corporate failure prediction, Omega, 2001.12,29 (6): 561-576
Beynon Malcolm, Reducts within the variable precision rough sets model: A further investigation, European Journal of Operational Research, 2001.11, 134 (3):592-605
Bobbio A, Franceschinis G, Gaeta R, et al, Exploiting Petri nets to support fault tree based dependability analysis, Proceedings. The 8th International Workshop on Petri Nets and Performance Models, 1999, pp: 146-155
Boris Kovalerchuk, Evgenii Vityaev, Data Mining in Finance: Advances in Relational and Hybrid Methods, Kluwer, Dordrecht, 2000
Boyd M.A, Iverson D.L, Digraphs and fault trees: a tale of two combinatorial modeling methods, Reliability and Maintainability Symposium, Proceedings,Annual, 1993, pp: 220-226
Brockmann W, Comparison of fuzzy, rule-based and conventional process control.Proc. IFAC/IFIP/IMACS Symposium on Artificial Intelligence in Real Time Control-AIRTC'94, Valencia, 1994, pp: 117-122,
Caines P.E, Wang S, COCOLOG.. a conditional observer and controller logic for finite machines , Proceedings of the 29th IEEE Conference on Decision and Control, 1990.12.5—7, 5: 2845—2850
Castro J.L, How many rules are necessary to get a "good" fuzzy controller for a control problem? 1997, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, 1997.7.1-5, 2:749-754
Chan C.C, Batur C, Srinivasan A, Determination of quantization intervals in rule based model for dynamic systems, 1991 IEEE International Conference on Systems, Man, and Cybernetics, 1991'Decision Aiding for Complex Systems,Conference Proceedings, 1991, 3:1719-1723
Chan Chieno-Chung, A rough set approach to attribute generalization in data mining, Journal of InformationSciences, 1998, 107:169-176
Changchien S.Wesley, Lu Tzu-Chuen, Mining association rules procedure to support on-line recommendation by customers and products fragmentation,Expert Systems with Applications 2001.5, 20 (4): 325-335
Chen W.C, Chang Ni-Bin, Chen Jeng-Chung, Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment, Water Research,2003.1, 37 (1): 95-107
Chen Xiang-hui, Zhu Shan-jun, Ji Yin-dong, Entropy based uncertainty measures for classification rules with inconsistency tolerance, IEEE International Conference on Systems, Man, and Cybemetics, 2000, 4:2816-2821
Chen Yi-Qiang, Gao wen, Zhu Ting-shao, et.al, Learning prosodic patterns for mandarin speech synthesis, Journal of Intelligent Information Systems, 2002,19 (1) : 95-109
Cheung D.W, Han Jia-wei, Ng V.T, et al, Maintenance of discovered association rules in large databases: an incremental updating technique, 1996. Proceedings of the Twelfth International Conference on Data Engineering, 1996, pp: 106-114
Chi S, Lee S, Park S, Automated generation of fault tree using the symbolic DEVS simulation, AI, Simulation, and Planning in High Autonomy Systems, 1993.
'Integrating Virtual Reality and Model-Based Environments'. Proceedings.Fourth Annual Conference , 1993.9.20-22, pp: 148-154
Ching Torng Lin, Mao Jiun Wang, Hybrid fault tree analysis using fuzzy sets,Reliability Engineering and System Safety , 1997, 58:205-213
Cho Young-wan, Lee Kuchul, Yoo Jaehyung, et al, Autogeneration of fuzzy rules and membership functions for fuzzy modeling using rough set theory, LEE Proceedings-Control Theory and Applications, 1998.9, 145 (5): 437-442
Choubey S.K, Deogun J.S, Raghavan V.V, et al, A comparison of feature selection algorithms in the context of rough classifiers, 1996, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996.9.8-11,2:1122-1128
Chung Hong, Choi Kyung-Oak, Chung Hwan-Mook, Generation of approximation rules using information gain, Fuzzy Systems Conference Proceedings, 1999, 3:1241-1245
Coppit D, Sullivan K.J, Dugan J.B, Formal seananties of models for computational engineering: a case study on dynamic fault trees, 2000. Proceeding. 11th International Symposium on Software Reliability Engineering, 2000, pp: 270-282
Covadonga Fernandez-Baizan M, Menasalvas Ruiz E, Pena Sanehez J, et al,Using RSDM to mine socio-economic indicators, 1997. 'Computational Cybernetics and Simulation', 1997 IEEE International Conference on Systems, Man, and Cybernetics, 1997.10.12-15, 2:1385-1389
Dai Jian-Hua, Li Yuan-Xiang, Study on discretization based on rough set theory,Proceedings. 2002 International Conference on Machine Learning and Cybernetics, 2002, 3: 1371-1373
Dai Jian-Hua, Li Yuan-Xiang, Heuristic genetic algorithm for minimal redact in decision system based on rough set theory, Proceedings. 2002 Internafional Conference on Machine Learning and Cybernetics, 2002, 2:833-836
De Vries R.C, An automated methodology for generating a fault tree , IEEE Transactions on Reliability, 1990.4, 39 (1): 76-86
Devedzic V, A survey of modem knowledge modeling techniques, Expert Systems with Applications, 1999.tl, 17 (4): 275-294
Dong Hai-ying, Zhang Yu-bo, Xue Jun-yi, Hierarchical fault diagnosis for substation based on rough set , Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 4:2318-2321
Dugan J.B, Venkataraman B, Gulati R, DIFtree: a software package for the analysis
of dynamic fault tree models, Reliability and Maintainability Symposium. 1997 Proceedings, Annual, 1997.1.13-16, pp: 64-70
Dugan J.B, Sullivan K.J, Coppit D, Developing a low-cost high-quality software tool for dynamic fault-tree analysis, IEEE Transactions on Reliability, 2000.3,49 (1): 49-59
Düntsch Ivo, Gediga Günther, Uncertainty measures of rough set prediction,Artificial Intelligence, 1998.11, 106 (1): 109-137
Elliott M.S, Computer-assisted fault-tree construction using a knowledge-based approach, IEEE Transactions on Reliability, 1994.4, 43 (1): 112-120
Felix R, Ushio T, Rule induction from inconsistent and incomplete data using rough sets, 1999 IEEE International Conference on Systems, Man, and Cybernetics,1999, 5: 154-158
Felix R, Ushio T, Rough sets-based machine learning using a binary discernibility matrix, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999, 1 : 299-305
Filev D, Rule-base guided adaptation for mode detection in process control, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th,2001.7.25-28, 2:1068-1073
Filippetti F, Franceschini G, Tassoni C, et al, Recent developments of induction motor drives fault diagnosis using AI techniques, IEEE Transactions on Industrial Electronics, 2000.10, 47 (5): 994-1004
Fogarty T.C, Learning control rules, 1989. Proceedings. ICCON'89. IEEE International Conference on Control and Applications, 1989, pp: 900-906
Frank P.M, Analytical and qualitative model-based fault diagnosis-A survey and some new results. In: European Journal of Control, 1996,2 (1): 6-23
Freeman R.L, Grzymala-Busse J.W, Riffel L.A, et al, Analyzing the relation between heart rate, problem behavior, and environmental events using data mining system LERS, CBMS 2001. Proceedings. 14th IEEE Symposium on Computer-Based Medical Systems, 2001, pp: 11-16
Fu Li-min, Incremental knowledge acquisition in supervised learning networks, IEEE Transactions on Systems, Man and Cybernetics, 1996.11, Part A, 26 (6):801-809
Fujimori S, Kaiya T, Inoue T, Analysis of discharge currents with discernibility matrices, Proceedings of 1998 International Symposium on Electrical Insulating
Materials, 1998, pp: 649-652
Gang Zhao, Kobayashi A, Sakai Y, Image information retrieval using rough set theory, Processing. 2002. Proceedings. 2002 International Conference on Image, 2002, 1 : 417-420
Geymayr J.A.B, Ebecken N.F.F, Fault-tree analysis: a knowledge-engineering approach, IEEE Transactions on Reliability, 1995.3, 44 (1): 37-45
Gmytrasiewicz P, Hassberger J.A, Lee J.C, Fault tree based diagnostics using fuzzy logic, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990.11,12 (11): 1115-1119
Golan R.H, Ziarko W, A methodology for stock market analysis utilizing rough set theory, Computational Intelligence for Financial Engineering, Proceedings of the IEEE/IAFE, 1995, pp: 32-40
Greco Salvatore, Matarazzo Benedetto, Slowinski Roman, Rough sets theory for multicriteria decision analysis, European Journal of Operational Research,2001.2.15, 129 (1): 1-47
Gu X.P, Tso S.K, Zhang Q, Combination of rough set theory and artificial neural networks for transient stability assessment, International Conference on Power System Technology, 2000, Proceedings, PowerCon, 2000, 1:19-24
Guan J.W, Bell D.A, Rough computational methods for information systems,Artificial Intelligence, 1998, 105:77-103
Guan J.W, Bell D.A, Guan Z, Matrix computation for information systems,Information Sciences, 2001.1, 131 (1): 129-156
Guth M.A.S, A probabilistic foundation for vagueness and imprecision in fault-tree analysis, IEEE Transactions on Reliability. 1991.12, 40 (5): 563-571
Han Bin, Wu Tie-Jun, Data mining in multisensor system based on rough set theory, American Control Conference, Proceedings of the 2001, 2001, 6:4427-4431
Hannu Salonena, Hannu Nurmi, A note on rough sets and common knowledge events, European Journal of Operational Research, 1999, 112:692-695
Harry R.Lewis, Christos H.Papadimitriou, Elements of the Theory of Computation,PrenticeHatl, Inc 1999
Hashemi R.R, Le Blanc L.A, Rucks C.T, et al, A hybrid intelligent system for predicting bank holding structures, European Joumal of Operational Research,1998.9.1, 109 (2): 390-402
Haspel D, The role of rule based control in the process industry, IEE Colloquium on
Exploiting the Knowledge Base: Applications of Rule Based Control, 1989.7.1-3
Hirotaka Nakayama, Yuichi Hattori, Renichi Ishii, Rule extraction based on Rough Set thory and its application to medical data analysis, Systems, Man, and Cybernetics, 1999, IEEE SMC'99 Conference Proceedings, 1999, 5:924-929
Hong Tzung-pei, Tseng Li-huei, Wang Shyue-liang, Learning rules from incomplete training examples by rough sets, Expert Systems with Applications,2002.5, 22 (4): 285-293
Hong T.P, Wang T.T, Wang S.L, et al, Learning a coverage set of maximally general fuzzy rules by rough sets, Expert Systems with Applications, 2000, 19:97-103
Hor Ching-lai, Crossley P, Dunand F, Knowledge extraction within distribution substation using rough set approach, Power Engineering Society Winter Meeting, 2002. IEEE, 2002, 1: 654-659
Hu Tao, Lu Bing-Chao, Chen Guang-ju, A rotary machinery fault diagnosis approach based on rough set theory, 2000. Proceedings of the 3rd World Congress on Intelligent Control and Automation, 2000, 1:685-689.
Hu Xiao-hua, Cercone N, Mining knowledge rules from databases: a rough set approach, Proceedings of the Twelfth International Conference on Data Engineering, 1996, pp: 96-105
Hu Xiao-hua, Cercone N, Learning maximal generalized decision rules via discretization, generalization and rough set feature selection, 1997. Proceedings, Ninth IEEE International Conference on Tools with Artificial Intelligence, 1997.11.3-8, pp: 548-556
Hu Xiao-hua, Knowledge discovery in databases: an attribute—oriented rough set approach, Ph.D dissertation, Regina University 1995
Huan Liu, Feature selection via discretization, IEEE Transaction on knowledge and data engineering, 1997.7-8, 9 (4) : 642-645
Huan Liu, Setiono R, Chi2: feature selection and discretization of numeric attributes, Seventh International Conference on Tools with Artificial Intelligence, 1995. Proceedings, 1995, pp: 388-391
Huang P, Li Z, The measure of effect in an expert system for automatic generation of fault trees , IEEE Transactions on Reliability, 1992.3, 41 (1): 57-62
Huang Chihpin, Liu Chi-Bing, Automatic control for chemical dosing in laboratory-scale coagulation process by using an optical monitor, Water
Research, 1996.8,30 (8): 1924-1929
Hunt K.J, induction of decision trees for rule-based modelling and control,Proceedings of the 1992 IEEE International Symposium on Intelligent Control,1992.8.11-13, pp: 306-311
Jagielska Ilona, Matthews Chris, Whitfort Tim, An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems, Neurocomputing,1999.2, 24 (1-3): 37-54
Jaroslaw Stepaniuk, Optimizations of rough set model, Fundamenta Informaticae,1998, 36 (2-3): 265-283
Kasabov N.K, Qun Song, DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Transactions on Fuzzy Systems, 2002.4. 10 (2): 144-154
Katzberg J.D, Katzberg P, Predicting factors affecting likelihoods in engineering problems, 1999 IEEE Canadian Conference on Electrical and Computer Engineering, 1999, 2:1046-1051
Khan Faisal I, Abbasi S.A, Analytical simulation and PROFAT Ⅱ: a new methodology and a computer automated tool for fault tree analysis in chemical process industries, Journal of Hazardous Materials, 2000.7.10, 75 (1): 1-27
Kilyoo Kim, Sang-Hoon Han, A fast method of finding minimal outsets in a risk Monitor, Reliability Engineering and System Safety, 1998, 62:17-21
King-Kwok Ng, Wai Lam, Updating of association rules dynamically, Proceedings. 1999 International Symposium on Database Applications in Non-Traditional Environments, 2000, pp: 84-91
King P.J, Burnham K.J, James D.J.G, A combined rule-based and model-based adaptive control scheme, Control'94. International Conference on Control,1994.3.21-24, 2: 1484-1489
Kneale J.B, Application of expert system techniques to fault tree diagnostic data,1997 IEEE Autotestcon Proceedings, 1997, pp: 100-104
KomorowskiJ, PawlakZ, Polkowski L, etal, Rough Sets: ATutorial, technical report, 1999.
Kryszkiewicz Marzena, Rules in incomplete information systems, Information Sciences, 1999.2, 113 (3-4): 271-292
Kryszkiewicz Marzena, Rough set approach to incomplete information systems,
Information Sciences, 1998 (112), 39-49
Kuzawinski K.M, Smurthwaite R, Automated fault tree analysis via AI/ES,Reliability and Maintainability Symposium, 1988. Proceedings, Annual, 1988.1,pp: 331-335
Lambert-Tortes G, da Silva A.P.A, Quintana V.H, et al, Classification of power system operation point using rough set techniques, 1996, IEEE International Conference on Systems, Man, and Cybernetics, 1996.10.14-17, 3:1898-1903
Lan Shu, Mo Zhi-Wen, Hu Dan, Methods of leaming rules based on rough set:LBR and LEM3, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001, 2:753-756
Lee Seungkoo, Vachtsevanos George, An application of rough set theory to defect detection of automotive glass, Mathematics and Computers in Simulation,2002.9.30, 60 (3-5): 225-231
Li De-yu, Ma Yi-chen, Invariant characters of information systems under some homomorphisms, Information Sciences, 2000, 129:211-220
Li Long-Shu, Tang Bin, Ni Zhi-Wei, et al, A study on spatial attribute data mining based on rough set, 2002.Proceedings.2002 International Conference on Machine Learning and Cybernetics, 2002, 2:743-746
Li Xiao-lei, Wu Xiao-bing , The application of rough set theory in vehicle transmission system fault diagnosis, Vehicle Electronics Conference, 1999. (IVEC'99) Proceedings of the IEEE International, 1999, 1: 240-242
Liang Ji-ye, Xu Zong-ben, Uncertainty measures of roughness of knowledge and rough sets in incomplete information systems, Proceeding of the 3rd World Congress on Intelligent Control and Automation, 2000.6.28--7.2, Hefei, P.R.China
Liggesmeyer P, Rothfelder M, Improving system reliability with automatic fault tree generation, Digest of Papers, Twenty-Eighth Annual Intemational Symposium on Fault-Tolerant Computing, 1998, pp: 90-99
Lingras P, Rough set clustering for Web mining, 2002. FUZZ-IEEE'02.Proceedings of the 2002 IEEE International Conference on Fuzzy Systems,2002, 2:1039-1044
Liu Shu-lin, Shi Wen-gang, Rough set based intelligence diagnostic system for valves in reciprocating pumps, 2001 IEEE International Conference on Systems, Man, and Cybernetics, 2001, 1:353-358
Luzeaux D, Process control and machine learning: rule-based incremental control, IEEE Transactions on Automatic Control, 1994.6, 39(6):1166—1171
Luzeaux D, Let's learn to control a system! 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on Systems, Man and Cybernetics, 1993.10.17-20, 3:199-204
Luzeaux D, Incremental rule-based control and learning, Proceedings of the Third Annual Conference of AI, Simulation and Planning in High Autonomy Syslems, 1992. Integrating Perception, Planning and Action, pp: 23-28
Mackinson Steven, An adaptive fuzzy expert system for predicting structure, dynamics and distribution of herring shoals, Ecological Modeling, 2000.2.28, 126(2-3): 155-178
Maddouri M, Elloumi S, Jaoua A, An incremental learning system for imprecise and uncertain knowledge discovery, Information Sciences, 1998.8, 109(1-4):149-164
Mak Brenda, Munakata Toshinori, Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3, European Journal of Operational Research 2002.1.1, 136(1): 212-229
Malcolm Beynon, Reducts within the variable Precision rough sets model: A further investigation, European Journal of Operational Research, 2001, 134: 592-605
Maudal O, Preprocessing Data for Neural Network Based Classifiers-Rough Sets Vs Principal Component Analysis, MSc Thesis, University of Edinburgh, 1996
McCluskey E.G, Thompson S, Rule-based adaptive PID control using a pattern recognition approach, IEE Colloquium on Expert Systems in Process Control, 1988.3.28
McKee Thomas E, Lensberg Terje, Genetic programming and rough sets: A hybrid approach to bankruptcy classification, European Journal of Operational Research, 2002.4.16, 138 (2): 436-451
Miao Duo-qian, Wang Jue, Information-based algorithm for reduction of knowledge, 1997, ICIPS 97, 1997, IEEE International Conference on Intelligent Processing Systems, 1997, 2:1155-1158
Michael Doumpos, Constantin Zopounidis, Rough sets and multivariate statistical classification: A simulationstudy, computational economics, 2002, 19:287-301
Mikami D, Hagiwara M, Self-growing learning vector quantization with additional learning and rule extraction abilities, 2000 IEEE International Conference.on Systems, Man, and Cybernetics, 2000, 4: 2895-2900
Milne Robert, Drummond Mike, Renoux Patrick, Predicting paper making defects on-line using data mining, Knowledge-Based Systems, 1998.11.23, 11(5-6): 331-338
Mohammed Javeed Zaki, Scalable Data Mining for Rules, Ph.D Dissertation, University of Rochester Rochester, New York, 1998
Mrozek A, Plonka L, Kedziera J, The methodology of rough controller synthesis, 1996., Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996.9.8-11, 2: 1135-1139
Munakata T, Rough control: A perspective, in T.Y. Lin and N. Cercone(Eds.), Rough Sets and Data Mining: Analysis of Imprecise Data, Kluwer Academic, 1997, pp: 77-88.
Munakata T, Pawlak Z, Rough control: application of rough set theory to control, Proceedings, Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, 1996.9.2-5, 1:209-218
Nathalie Ch_ever, Franc_ois Gagn_e, et al, Application of rough sets analysis to identify pollutedaquatic sites based on a battery of biomarkers: a comparison with classical methods, Chemosphere, 2003, 51:13-23
Nguyen Hoang Phuong, Le Linh Phong, Santiprabhob P, et al, Approach to generating rules for expert systems using rough set theory, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001.7.25-28, 2:877-882
Nguyen H.S, Skowron A, Quantization of real values attributes, rough set and boolean reasoning approaches. Proc of the Second Joint Conference on Information Sciences, 1995.10, Wrightsville Beach, NC, pp:34-37.
Nguyen H S, Dscretization of real value attributes: A boolean reasoning approach, Warsaw: University of Poland, 1997
Nguyen H.S, Nguyen S.H, Discretization of Real Value Attributes for control problems, Workshop on Robotics, Inteligent Control, and Decision Support systems. PJWSTK, Warsaw, Poland, 1999
Nguyen S.H, Nguyen H.S, Some efficient algorithms for rough set methods, In Proceedings IPMU'96 Granada(Spain), pp. 1541-1457
Nils J Nilsson, Artificial Intelligence, A New Synthesis, China Machine Press,
1999
Nong Ye, The presentation of knowledge and state-Information for system fault diagnosis, IEEE transaction on Reliability, 1996.12, 45 (4): 638-645
Ou Lian, Sun Cai-xin, Bi Wei-min, et al, A steam turbine-generator vibration fault diagnosis method based on rough set, 2002. Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 3:1532-1534
Pal S.K, Mitra P, Multispectral image segmentation using the rough-set-initialized EM algorithm, IEEE Transactions on Geoscience and Remote Sensing, 2002.11, 40 (11):2495-2501
Pan HanSuk, Yun WonYoung, Fault tree analysis with fuzzy gates, Computers ind.Engng, 1997, 33(3-4): 569-572
Park Sang C, Piramuthu Selwyn, Shaw Michael J, Dynamic rule refinement in knowledge-based data mining systems, Decision Support Systems, 2001.6, 31(2):205-222
Pawlak Z, Rough set theory for intelligent industrial applications, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999, 1:37-44
Pawlak Z, Granularity of knowledge, Indiscemibility and rough sets, Fuzzy Systems Proceedings, 1998, IEEE World Congress on Computational Intelligence, 1998, 1:106-110
Pawlak Z, Why Rough Sets? Proc Of the fifth IEEE Inter conf on Fuzzy Systems, 1996, 2:738-743
Pawlak Z, Rough sets, decision algorithms and bayes' theorem, European journai of operational research, 2002, 136:181-189
Pawlak Z, Rough sets and intelligent data analysis, Information Sciences, 2002.11, 147(1-4): 1-12
Pawlak Z, Rough sets and data analysis, Fuzzy Systems Symposium, Soft Computing in Intelligent Systems and Information Processing, Proceedings of the 1996 Asian, 1-6
Pawlak Z, Rough set, Theoretical aspects of reasoning about data, Dordrecht. The Netherlands: KluwerAcademic Publishers, 1991
Pedrycz W, Granular computing: an introduction, IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, 2001, 3:1349-1354
Pedrycz W, Michael H Smith, Information granulation: Percepts and their stability, 2000, The Ninth IEEE International Conference on Fuzzy System, 2000, 1:
83-88
Pedrycz W, Han L, Peters J.F, Ramanna S, et al, Calibration of software quality: Fuzzy neural and rough neural computing approaches, Neurocomputing, 2001.2, 36 (1-4): 149-170
Peters J, Skowron A, Stepaniuk J, Information granules in spatial reasoning, IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, 2001, 3:1355-1360
Peters J.F, Ziaei K; Generating rules in selecting controller gains: a combined rough sets/fuzzy sets approach, IEEE Canadian Conference on Electrical and Computer Engineering, 1998, 1:233-236
Peters J.F, Skowron A, Suraj .Z, An application of rough set methods in control design, Fundamenta Informaticae, 2000, 43 (1-4): 269-290
Peters J.F, Pawlak Z, Skowron A, A rough set approach to measuring information granules, Computer Software and Applications Conference, 2002.Proceedings. 26th Annual International, 2002, pp: 1135-1139
Peters J.F, Ramanna S, Borkowski M, et al, Sensor fusion: a rough Petri net model, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001.7.25—28, 5:2882—2887
Peters L, Beck K, Camposano R, Fuzzy logic controller with dynamic rule set, Proceedings of the 1992 IEEE International Symposium on Intelligent Control, 1992, pp:216-219
Polkowsk L, Skowron A, Towards adaptive calculus of granules, Fuzzy Systems Proceedings, The 1998 IEEE International Conference on Computational Intelligence, 1998, 1:111-116,
Questier F, Arnaut-Rollier I, Walczak B, et al, Application of rough set theory to feature selection for unsupervised clustering, Chemometrics and Intelligent Laboratory Systems 2002.9.28, 63 (2): 155-167
Radzikowska Anna Maria, Kerre Etienne E, A comparative study of fuzzy rough sets, Fuzzy Sets and Systems, 2002.3.1, 126(2):137-155
Ramanna S, Peters J.F, Ahn T, Software quality knowledge discovery: a rough set approach, Computer Software and Applications Conference, 2002. Proceedings. 26th Annual International, 2002, pp: 1140-1145
Ren Yansong, Bechta Dugan J, Design of reliable systems using static and dynamic fault trees, IEEE Transactions on Reliability, 1998.9, 47(3):234-244
Rivals I, Personnaz L, Internal model control using neural networks, ISIE 96,
Proceedings of the IEEE International Symposium on Industrial Electronics, 1996.6.17—20, 1: 109—114
Roslyn M.Sinnamon, John D.Andrews, Fault Tree Analysis and Binary Decision Diagrams, 1996 Proceeding Annual Reliability and Maintainability Symposium, 1996, pp:215-222
Roy Amitava, Pal Sankar K, Fuzzy discretization of feature space for a rough set classifier, Pattern Recognition Letters, 2003.3, 24(6):895-902
Sarda N.L, Srinivas N.V, An adaptive algorithm for incremental mining of association rules, Proceedings. Ninth International Workshop on Database and Expert Systems Applications, 1998, pp:240-245
Seungkoo Lee, An Architecture for a Diagnostic/Prognostic System with Rough Set Feature Selection and Diagnostic Decision Fusion Capabilities, Ph.D dissertation, Georgia Institute of Technology, 2002
Shah N, Ziarko W, Data-based acquisition and incremental modification of classification rules, Computational Intelligence, 1995, 11:357-370
Shao J, Finding reducts with user specified criteria, 1997. Proceedings., Eighth International Workshop on Database and Expert Systems Applications, 1997.9.1-2, pp: 352-357
Shen Li-xiang, Francis E.H.Tay, et al, Fault Diagnosis using Rough Sets Theory, Computers in Industry, 2000, 43:61-72
Shen Q, Chouchoutas A, FuREAP: a Fuzzy-Rough Estimator of Algae Populations, Artificial Intelligence in Engineering, 2001.1, 15(1):13-24
Shen Qiang, Chouchoulas Alexios, A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems, Engineering Applications of Artificial Intelligence, 2000.6.1, 13(3):263-278
Shi Wen-gang, Wang Ri-xin, Huang Wen-hu, Application of rough set theory to fault diagnosis of check valves in reciprocating pumps, 2001.Canadian Conference on Electrical and Computer Engineering, 2001, 2:1247-1250
Shu Hongehun, Sun Xiangfei, Si Dajun, Study of fuzzy controller for voltage and reactive power in substation using rough set theory, 2002. Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 4:2452-2456
Shu Hong-chun, Sun Xiang-fei, Si dajun, A new method for locating faults on transmission lines based on rough set and FNN, 2002. Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 4:2584
-2588
Sinnamon R.M, Andrews J.D, Fault tree analysis and binary decision diagrams, Reliability and Maintainability Symposium, 1996 Proceedings. 'International Symposium on Product Quality and Integrity', Annual, 1996.1.22-25, pp: 215-222
Skowron Andrzej, Polkowski Leeh, Rough mereological foundations for design, analysis, synthesis, and control in distributed systems, Information Sciences, 1998.1, 104(1-2):129-156
Slowinski R, Vanderpooten D, A generalized definition of rough approximations based on similarity, IEEE Transactions on Knowledge and Data Engineering, 2000.3-4, 12 (2): 331-336
Son H Nguyen, Skowron A, Boolean reasoning for feature extraction problems., Tenth International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'97), (1997) October15-18, Charlotte, NC, USA, Lecture Notes in Artificial Intelligence 1325, Springer-Verlag, Berlin, 1997, pp:117-126.
Sridhar D.V, Bartlett E.B, Seagrave R.C, An information theoretic approach for combining neural network process models, Neural Networks, 1999.7, 12 (6): 915-926
Sridhar D. V, Seagrave R. C, Bartlett E. B, Process modeling, using stacked neural networks. AIChE J, 1996, 42:2529.
Sugeno M, Tanaka K, A fuzzy-logic-based approach to qualitative modeling, IEEE Trans on Fuzzy Systems, 1993, 1(1):7-31
Sun Ying-kai, Cao Long-han, Cao Chang-xiu, Slab defect diagnosis and predicting system in CC processing based on rough set theory, 2000. Proceedings of the 3rd World Congress on Intelligent Control and Automation, 2000, 1:673-675
Susmaga Robert, Analyzing discretizations of continuous attributes given a monotonic discrimination function, Intelligent Data Analysis, 1997, 1 (1-4): 157-179
Swiniarski Roman W, Skowron Andrzej, Rough set methods in feature selection and recognition, Pattern Recognition Letters, 2003.3, 24 (6):833-849
Tan Tian-Le, Song Zhi-Huan, Li Ping, Matrix computation for data cleaning and rule extraction in knowledge System, Proceedings of 2002 International Conference on Machine Learning and Cybernetics, 2002, 1:116—120
Tay F.E.H, Shen Li-xing, A modified Chi2 algorithm for discretization, IEEE
Transactions on Knowledge and Data Engineering, 2002.5-6, 14 (3): 666-670
Tay F.E.H, Shen Li-xing, Economic and nancial prediction using rough sets model, European Journal of Operational Research, 2002, 141:641-659
Therasa Beaubouef, Frederick E Petry, Gurdial Arora, Information-theoretic measures of uncertainty for rough sets and rough relation databases, Journal of Information Sciences, 1998, 109:185-195
Tong Ling-yun; An Li-ping, Incremental learning of decision rules based on rough set theory, 2002. Proceedings of the 4th World Congress on Intelligent Control and Automation, 2002, 1: 420-425
Tsau Young, Lin T.Y, Fuzzy sets, rough set and probability, Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American, 2002, pp: 302—305
Tsumoto S, Statistical evidence for rough set analysis, 2002. FUZZ—IEEE'02. Proceedings of the 2002 IEEE International Conference on Fuzzy Systems,2002, 1: 757—762
Tsumoto S, Automated discovery of positive and negative knowledge in clinical databases, IEEE Engineering in Medicine and Biology Magazine, 2000.7-8,19 (4): 56-62
Tsumoto S, Rule induction system based on characterization of medical diagnosis using rough sets IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001, 4:2364-2369
Tsumoto S, Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic, Information Sciences, 2000, 124:125-137
Tsumoto S, Discovery of rules for medical expert systems-rough set approach,1999.JCCIMA '99. Proceedings. Third International Conference on Computational Intelligence and Multimedia Applications, t999, pp: 2t2-216
Tsumoto S, Tanaka H, Extraction of domain knowledge from databases based on rough set theory, 1996, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996.9.8—11, 2:748-754
Wang Guo-Yin, Zheng Zheng, Zhang Yi, Ridas-a rough set based intelligent data analysis system, 2002.Proceedings. 2002 International Conference on Machine Learning and Cybernetics, 2002, 2:646-649
Wang Yi-Fan, Mining stock price using fuzzy rough set system, Expert Systems with Applications, 2003.1, 24 (1): 13-23
Wang Zhi-liang, Meng Wen-bo, Gu Xue-jing, et al, The research of the policeGIS spatial data classification technology based on rough set,2002.Proceedings of the 4th World Congress on Intelligent Control and Automation,2002, 1:432-436
Way Yuan-Shun, Hsia Der-Yu, A simple component-connection method forbuilding binary decision diagrams encoding a fault tree, Reliability Engineeringand System Safety, 2000.10, 70 (1): 59-70
Wei Jin-mao, Huang Dao, Knowledge degeneration method based on rough set,Proceeding of 3~(rd) Asian Control Conference, 2000.7.4-7, Shanghai, pp: 2429-2434
Wen Hao, A knowledge discovery method and its industrial applications,Proceedings of the 3rd World Congress on Intelligent Control and Automation,2000, 1:369-372
Wolpert D.H, Stacked generalization, Neural Networks, 1992, 5 (2): 241.
Woon Y.K, Wee-Keong Ng, Das A, Fast online dynamic association rule mining,Proceedings of the Second International Conference on Web InformationSystems Engineering, 2001, 1: 278-287
Valentin N, Denoeux T, A neural network-based software sensor for coagulationcontrol in a water treatment plant, Intelligent Data Analysis, 2001, 5:23-39
Xu Xiu-ping, Peters J.F, Rough set methods in power system fault classification,2002. IEEE CCECE 2002. Canadian Conference on Electrical and ComputerEngineering, 2002, 1:100-105
Xu Z.B, Liang J.Y, Dang C.Y, et al., Inclusion degreee: a perspective on measuresfor rough set data analysis, Information sciences, 2002, 141:227-236
Yao Y, Rough sets, neighborhood systems and granular computing, 1999 IEEECanadian Conference on Electrical and Computer Engineering, 1999, 3:1553-1558
Yao Y.Y, Constructive and algebraic methods of the theory of rough sets,Information Sciences, 1998.8, 109 (1—4): 21—47
Yao Y.Y, Stratified rough sets and granular computing, Fuzzy InformationProcessing Society, 1999, NAFIPS, 18th International Conference of the NorthAmerican, 1999, pp: 800-804
Yao Y.Y, A comparative study of fuzzy sets and rough sets, Information Sciences,1998.8, 109 (1-4): 227-242
Yao Y.Y, Wong S.K.M, Generalized probabilistic rough set models, Fuzzy Systems
Symposium, 1996. 'Soft Computing in Intelligent Systems and InformationProcessing', Proceedings of the 1996 Asian, 1996.12.11-14, pp: 158-163
Yeung Yam, Nguyen H.T, Kreinovich V, Multi-resolution techniques in the rules-based intelligent control systems: a universal approximation result,Proceedings of the 1999 IEEE International Symposium on IntelligentControl/Intelligent Systems and Semiotics, 1999, pp: 213-218
Yoon J.P, Techniques for data and rule validation in knowledge based systems,Computer Assurance, COMPASS '89, Proceedings of the Fourth AnnualConference on Systems Integrity, Software Safety and Process Security,1989.6.19-23, pp: 62-70
Yuan B, Wang X.Z, Morris T, Software analyser design using data miningtechnology for toxicity prediction of aqueous effluents, Waste Management,2000.12, 20 (8): 677-686
Zhang Qi, Han Zhen-xiang, Wen Fu-shuan, A new approach for fault diagnosisin power systems based on rough set theory[A]. Fourth International Conferenceon Advances in Power System Control, Operation and Management, 1997, 2:597-602.
Zhang Qi, Han Zhen-xiang, Wen Fu-shuan, A new approach for fault diagnosisin power systems based on rough set theory, 1997. APSCOM-97. FourthInternational Conference on Advances in Power System Control, Operation andManagement (Conf. Publ. No. 450),1997.11.14, 2:597-602
Zhong Ning, Dong Ju-zhen, Ohsuga S, et al, An incremental, probabitistic roughset approach to rule discovery, Fuzzy Systems Proceedings, 1998, IEEE WorldCongress on Computational Intelligence, 1998, 2:933-938
Zhong Ning, Dong Ju-zhen, Ohsuga Setsuo, Rule discovery by soft inductiontechniquesm, Neurocomputing, 2001.2, 36 (1-4): 171-204
Zhong Ning; Dong Ju-zhen, Liu Chun-nian, et al, A hybrid model for rulediscovery in data, Knowledge-Based Systems, 2001.11.1, 14 (7): 397-412
安利平，刘喜华等，不相容决策系统中获取规则的粗糙集方法，青岛大学学报：工程技术版，2002，17(1)：53—55
安利平，仝凌云，粗糙集理论中一种属性离散化算法，河北工业大学学报，2002，31(3)：39—43
卞学海，非一致性数据的必然规则学习，华东船舶工业学院学报，1998，12(1)：25—30
白桦，李圭白，混凝投药的神经网络控制方法，给水排水，2001，27(11)：83
—86
毕建东，曲复宛，一个机器学习定量属性的定性方法TCIN，哈尔滨工业大学学报，1997．10，29(5)：73—76
蔡智，王煦法等，基于粗糙集理论的连续属性离散化算法研究，计算机科学，2001．5(专刊)，28(5)：39—41
曹龙汉，孙颖楷等：基于粗糙集理论的连铸坯缺陷诊断预报系统，重庆大学学报(自然科学版)，2001，24(1)：95—98
曹龙汉，曹长修，基于粗糙集理论的柴油机神经网络故障诊断研究，内燃机学报，2002，20(4)：357—361
曹长修，孙颖楷等，基于粗糙集理论的内燃机故障诊断专家系统，重庆大学学报(自然科学版)，2001，24(4)：45—47
陈秉正，韩春鹏，归纳式学习中连续型数据的区间划分问题，系统工程理论与实践，2001．4，4：1—17
陈德刚，张文修，粗糙集和拓扑空间，西安交通大学学报，2001，35(12)：1313—1315
陈海，粗糙集理论在内燃机柱塞故障诊断中的应用，贵州工业大学学报：自然科学版，2002，31(1)：46—49
陈湘晖，朱善君等，扩展的粗糙集模型及其不确定性量度，清华大学学报：自然科学版，2002，42(1)：128—131
程明华，姚一平，动态故障树分析方法在软、硬件容错计算机系统中的应用，航空学报，2000，11(1)：34—37
程岩，黄梯云，基于Rough Set理论发现最小归纳依赖关系的方法研究，计算机工程，2000，26(3)：26—27，4
杜海峰，丁国锋等，基于粗糙集与模糊神经网络的多级压缩机诊断，西安交通大学学报，2001，35(9)：940—944
范世栋，支天云，粗糙集的代数性质，山西大学学报：自然科学版，2001，24(2)：116—119
冯玉才，冯剑琳，关联规则的增量式更新算法，软件学报，1998．4，9(4)：301—306
冯纯伯，费树岷，非线性控制系统分析与设计(第2版)，北京，电子工业出版社，1998
高峰，谢剑英，发现关联规则的增量式更新算法，计算机工程，2000，26(12)：49—50，112
桂修文，郑尚龙等，知识库的一致性与完备性检验，华中理工大学学报：增刊(Ⅱ)，1991，19(A02)：33—39
杭小树，熊范纶，基于粗糙集理论的示例学习研究，浙江大学学报：理学版，2002，29(3)：346—354
韩斌，基于数据挖掘的信息融合理论和应用，浙江大学博士学位论文，杭州，2002
韩祯祥，张琦，粗糙集理论及其应用综述，控制理论与应用，1999，16(2)：153—157
韩祯祥，张琦，粗糙集理论及其应用，信息与控制，1998，27(1)：37—45
郝丽娜，徐心和，粗糙集理论在智能故障诊断中的应用，中国机械工程，2002，13(21)：1856—1858
郝丽娜，徐心和，基于粗糙集理论的规则修正方法，控制与决策，2002，17(5)：614—616，620
洪立，王世渊，王先来，神经网络控制器在未建模型的非线性系统中的应用，自动化与仪器仪表，2000．3，pp：4—6
缑锦，叶东毅，基于概念格的求所有绝对属性约简的一个算法，福州大学学报：自然科学版，2002，30(3)：298—300
侯利娟，王国胤等，粗糙集理论中的离散化问题，计算机科学，2000，27(12)：89—94
候忠生，非参数模型及其自适应控制理论，北京，科学出版社，1999
胡静，曹先彬等，基于相容粗糙集的图形图像信息预检索，计算机辅助设计与图形学学报，2002，14(3)：242—246
胡涛，吕炳朝等，一种基于粗糙集理论的燃气轮发电机组故障诊断新方法，电子测量与仪器学报，2001．3，15(1)：11—16
胡涛，吕炳朝等，基于粗糙集的不确定知识表示方法，计算机科学，2000，27(3)：90—93
胡寿松，自动控制原理，北京，国防工业出版社，1994
胡可云，陆玉昌等，粗糙集理论及其应用进展，清华大学学报：自科版，2001，41(1)：64—68
胡可云，陆玉昌等，基于概念格的分类和关联规则的集成挖掘方法，软件学报，2000，11(11)：1478—1484
黄冬梅，高印芝等，模糊决策树归纳算法及应用，河北师范大学学报：自然科学版，1999．6，23(2)：173—176
黄沛，李剑，基于粗糙集的保险风险规则挖掘模型，系统工程，2002，20(5)．34—39
黄曾麟，粗集理论及其应用，重庆大学出版社，重庆，1998
黄文虎，纪常伟等，基于故障树模型的智能诊断的确定性推理，机械强度，
1995．5，17(2)：38—42
贾要勤，徐光华等，粗糙集属性约简的基因算法，中国机械工程，2000．7，11(7)：797—800
廖志伟，张雅明等，基于数据挖掘模型的配电网故障定位诊断，天津大学学报：自然科学与工程技术版，2002，35(3)：322—326
黎明，张化光，基于粗糙集的神经网络建模方法研究，自动化学报，2002，28(1)．—27—33
厉玉鸣，瓮维勤，非线性控制系统，化学工业出版社，北京，1985
李龙澍，程慧霞等，基于凸Rough集的数据约简和规则发现研究，东南大学学报：自然科学版，2002，32(2)：201—205
李玉榕，乔斌等，基于熵的粗糙集属性简约算法，电路与系统学报，2002，7(3)：8—12
李永敏，朱善君，根据粗糙集理论进行BP网络设计的研究，系统工程理论与实践，1999，19(4)：62—69
李文，孙辉，陈善本，一种建立模糊模型的粗糙集方法，控制理论与应用，2001．2，18(1)：69—75
刘健勤，粗糙集理论及其最新进展，计算技术与自动化，1998．3，17(1)：43—48
刘宜平，沈毅等，设备故障诊断的粗糙集方法研究，高技术通讯，2001，5：77—79
刘宜平，沈毅等：一种基于粗糙集理论的容错网络实现及其在故障诊断中的应用，电机与控制学报，2000，4(2)：117—121
刘业政，杨善林等，粗糙集数据分析的计算方法，合肥工业大学学报：自然科学版，2002，25(2)：161—166
刘振华，刘三阳，基于粗糙集的采煤方式选择模型，计算机与现代化，2002．9，85(9)：1—3
刘震宇，郭宝龙等，一种新的用于连续值属性离散化的约简算法，控制与决策，2002，17(5)：545—549
刘宗田，属性最小约简的增量式算法，电子学报，1999，27(11)：96—98
刘清，Rough集及Rough推理，科学出版社，2001
卢荣德，陈宗海等，思维模拟在复杂工业过程建模中应用的综述，信息与控制，2002，31(4)：346—352
梁吉业，孟晓伟，信息熵在粗糙集理论中的应用，山西大学学报：自然科学版，2002，25(3)：281—284
粱吉业，徐宗本等，包含度与粗糙集数据分析中的度量，计算机学报，2001．5，
24(5)：544—547
马志峰，邢汉承等，决策表中规则获取的不确定行研究，控制与决策，2000，15(6)：703—707
马志锋，刑汉承等，粗糙控制中的规则获取策略研究，工业仪表与自动化装置，2000．6，PP：58—61
马元元，孙志挥等，时态数据库中增量关联规则的挖掘，计算机研究与发展，2000．12，37(12)：1446—1451
苗夺谦，范世栋，知识的粒度计算及其应用，系统工程理论与实践，2002， 22(1)：48—56
苗夺谦，Rough Set理论中连续属性的离散化方法，自动化学报，2001．5，27(3)：296—302
苗夺谦，胡桂蓉，知识约简的一种启发式算法，计算机研究与发展，1999．6，36(6)：681—684
苗夺谦，王珏，粗糙集理论中概念与运算的信息表示，软件学报，1999，10(2)：113—116
苗夺谦，王珏，粗糙集理论中知识粗糙性与信息熵关系的讨论，模式识别与人工智能，1998，11(1)：34—40
苗夺谦，王珏，基于粗糙集的多变量决策树构造方法，软件学报，1997．6，8(6)：425—431
权光日，刘文远等，连续属性空间上的规则学习算法，软件学报，1999：11，10(11)：1225—1232
乔斌，郑洪涛等，针对信息系统不完备性的粗糙集分层递阶约简，电路与系统学报，2001．6，2(2)：78—82
石冰，郑燕峰，改进型关联规则增量式更新算法与实现，小型微型计算机系统，2000，21(12)：1327—1329
石云，孙玉芳，基于Rough Set的空间数据分类方法，软件学报，2000,11(5)：673—678
史忠植，知识发现，清华大学出版社，2002，1
史定华，王松瑞，故障树分析技术方法和理论，北京，北京师范大学出版社，1993．11
苏健，高济，基于元信息的粗糙集规则增式生成方法，模式识别与人工智能，2001．12，14(4)：428—433
孙颖楷，张邦礼等，基于粗糙集理论的人工神经网络故障诊断系统，重庆大学学报：自然科学版，2000，23(6)：53—55
余彬海，周祖德，面向故障树结构的不精确知识管理，华中理工大学学报，1995，
23(2)：27—29
束洪春，司大军等，电力变压器故障诊断专家系统知识库建立和维护的粗糙集方法，中国电机工程学报，2002，22(2)：31—35
陶兰，唐玉荣，时态数据库周期规律与关联规则的挖掘，中国农业大学学报，2001．6，4：76—80
唐建国，粗糙集理论决策推理时样本缺损的处理方法，重庆三峡学院学报，2000，16(6)：78—80
谭天乐，宋执环，李平，信息系统数据清洗、规则提取的矩阵算法，信息与控制，2003．4，
谭天乐，宋执环，李平，基于粗糙集的故障诊断方法，浙江大学学报：工学版，2003．1，37(1)：47—50
吴今培，肖健华，智能故障诊断与专家系统，北京，科学出版社，1997
汪德澍，黄明瑞，非线性控制系统引论，成都，成都电讯工程学院出版社，1988
王珏，王任等，基于Rough Set理论的“数据浓缩”，计算机学报，1998．5，21(5)：393—400
王海清，工业过程监测：基于小波和统计学的方法，浙江大学博士学位论文，杭州，2000
王卫平，李熙亚等，基于粗糙集的数控机床故障诊断最小化方法，组合机床与自动化加工技术，2001，pp：31—34
王骥程，过程动态模型，杭州，浙江大学出版社，1994
王君圣，李敏强等，粗糙集中决策规则集的综合评判生成方法，第三届国际智能控制与自动化会议，中国安徽，合肥，2000，6．28—7．2
王国胤，Rough集理论与知识获取，西安交通大学出版社，2001。
王继承，朱明富，VDM算法在数据采掘中的应用，华中科技大学学报，2001．6，增刊，pp：90—92
王清毅，蔡智等，部分数据缺失环境下的知识发现方法，软件学报，2001，12(10)：1516—1524
王彬，熵与信息，西北工业大学出版社，西安，1994，10
王翔，袁兆山，多层次关联规则的增量式更新算法，合肥工业大学学报：自科版，2000，23(2)：200—204
王志海，胡可云，基于粗糙集合理论的知识发现综述，模式识别与人工智能，1998，11(2)：176—183
许志兴，伍华林等，基于信息熵的连续属性自动聚类算法，南京航空航天大学学报，2001．6，33(3)：233—236
夏雨佳，李少远等，一种基于粗糙集的信息系统决策规则提取方法，控制与决
策，2001，16(5)：577—580
谢志鹏，刘宗田，概念格与关联规则发现，计算机研究与发展，2000，37(12)：1415—1421
谢克明，杨静，粗糙集理论及其在智能控制领域的应用前景，大连理工大学学报，1999．7，30(4)：338—342
谢克明，侯宏仑，复杂系统的智能控制方法，太原理工大学学报，1998，29(6)：568—572，576
许志兴，陆金桂等，一种基于粗糙集理论的粗糙神经网络构造方法，南京航空航天大学学报，2001，33(4)：355—359
徐一新，叶东毅，知识约简的差别矩阵启发式算法，福州大学学报，2000．6，28(3)：120—123
徐章遂，最大故障信息量二元树诊断法，无线电通信技术，1989，15(2)：28—38
肖勇，陈意云，用遗传算法构造决策树，计算机研究与发展，1998．1，35(1)：49—52
顼晓娟，谢克明，基于粗糙集神经网络PID的串级控制系统设计，太原理工大学学报，2002，33(6)：610—612
叶东毅，基于粗糙隶属函数的规则组合可信度计算与推理，福州大学学报：自然科学版，2002，30(3)：294—297叶东毅，信息表相对属性约简的一个算法，福州大学学报：自然科学版，2000．12，28(6)：7—9
叶东毅，陈昭炯，一个改进的粗糙集属性约简算法，福州大学学报：自然科学版，2002，28(5)：9—12
袁小宏，赵仲生等，粗糙集理论在机械故障诊断中的应用研究，西安交通大学学报，2001，35(9)：954—958
于金龙，李晓红等，连续属性值的整体离散化，哈尔滨工业大学学报，2000．6，32(3)：48—53
杨会志，于春田，一种浮选过程控制的新策略—粗糙控制，矿业研究与开发，1999．10，19(5)：20—23
虞和济，候广琳，故障诊断的专家系统，北京，冶金工业出版社，1991
张飞弓，叶东毅，粗糙集连续属性量化算法的灵敏度分析，福州大学学报：自科版，2000，28(3)：28—30
张继勇，陆继远等，基于粗集方法的模糊控制器的优化设计，电气自动化，2002，24(2)：7—9
张文宇，薛惠锋等，粗糙集在数据挖掘分类规则中的应用研究，西北工业大学学报，2002，20(3)：430—434
张文修，吴伟志等，粗糙集理论与方法，北京，科学出版社，2001
张学民，安利平，动态数据库规则获取和更新的粗糙集方法，天津大学学报，2001．7，34(4)：529—532
张琦，韩祯祥等，一种基于粗糙集理论的电力系统故障诊断和警报处理新方法，中国电力，1998，31(4)：32—38
周东华，叶银忠，现代故障诊断与容错控制，清华大学出版出版社，2000．6
周经纶，孙权，一种故障树分析的新算法，模糊系统与数学，1997，11(3)：74—78
周青龙，故障诊断与监控，北京，兵器工业出版社，1992
周育健，王珏，RSL：基于Rough Set的表示语言，软件学报，1997，8(8)：569—576
邹先霞，杜威等，一种基于粗集理论的概念格构造方法，华中师范大学学报：自科版，2001，35(2)：146—149
赵卫东，盛昭瀚等，粗糙集在决策树生成中的应用，东南大学学报：自然科学版，2000．7，30(4)：132—137
朱玉，王先来，对未建模的工业过程对象进行控制的新方法，自动化与仪器仪表，2002．3，pp：35—39
Aleksander hrn, Rough logic control: A new approach to automatic control? Knowledge systems group, Fculty of Electrical Engineering and Computer Science, Norwegian Institute of Technology, University of Trondheim,Trondheim, Norway, 1993, 4, 29
Alonge F, D'Ippolito F, Pizzuto G, et al, Identification of nonlinear vectorial functions by means of rough set and fuzzy logic theories, Proceedings of the 1998 IEEE International Conference on Control Applications, 1998, 2:942-947
Amin Mousavi, Parviz Jabedar-Maralani, Double-faced rough sets and rough communication, Information Sciences, 2002, 148:41-53
An A, Chan C, Shan N, et al, Applying knowledge discovery to predict water-supply consumption, IEEE Expert [see also IEEE Intelligent Systems], 1997.7,12 (4): 72-78
Anantaram C, Nagaraja G, Non K.V, Verification of accuracy of rules in a rule based system Data & Knowledge Engineering, 1998, 27:115-138
Anderson K.L, Blankenship G.L, Lebow L.G, A rule-based adaptive PID controller, Proceedings of the 27th IEEE Conference on Decision and Control, 1988.12.7-9, 1:564-569
Andrzej Czyzewski, Automatic identication of sound source position employing neural networks and rough sets, Pattern Recognition Letters, 2003, 24:921-933
Andrzej Skowron, Zbigniew Suraj, A parallel algorithm for real-time decision making: A rough set apporach, Journal of Intelligent Information Systems (JIIS), 1996.9, 7 (1): 5-28
Arima M, Hara E.H, Katzberg J.D, A fuzzy logic and rough sets controller for HVAC systems, WESCANEX 95. Communications, Power, and Computing, Conference Proceedings. IEEE , 1995.5, 1: 133-138
Arkadiusz Wojna, Constraint based incremental learning of classification rules,
Lecture Notes in Artificial Intellingence, 2000.1, pp: 0428,
Bakar A.A, Sulaiman M.N, Othman M, et al, Finding minimal reduct with binary integer programmingindatamining, TENCON2000, Proceedings, 2000, 2:141-146
Batur C, Srinivasan A, Chan C.C, Automated rule based model generation for uncertain complex dynamic systems, Int. J. Of Engineering Applications of Artificial Intelligence, 1991, 4 (5): 359-366
Bautista R, Millan M, Diaz J.F, An efficient implementation to calculate relative core and reducts, Fuzzy Information Processing Society, NAFIPS. 18th International Conference of the North American , 1999.7, pp: 791-794
Bazan J, Skowron A, Synak P, Dynamic reducts as a tool for extracting laws from decision tables, In Proc: the Syrup. Methodologies for Intelligent Systems, Charlotte, NC, Lecture Notes in Artificial Intelligence, Berlin, pringerVerlag, 1994, pp: 346-355.
Beaubouef T, Petry F.E, A rough set foundation for spatial data mining involving vague regions, FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, 2002, 1: 767-772
Beaubouef T, Petry F, Vague regions and spatial relationships" A rough set approach, Fourth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'01 ), YokosukaCity, Japan, 2001.10.30-11.1, pp: 313-318
Beniaminy I, Kanonicz G, Neuhaus R, Creating an advanced diagnostic knowledge base from existing fault trees: a case study, AUTOTESTCON'99. IEEE Systems Readiness Technology Conference, 1999, pp: 153-159
Beynon Malcolm J, Peel Michael J, Variable precision rough set theory and data discretisation: an application to corporate failure prediction, Omega, 2001.12,29 (6): 561-576
Beynon Malcolm, Reducts within the variable precision rough sets model: A further investigation, European Journal of Operational Research, 2001.11, 134 (3):592-605
Bobbio A, Franceschinis G, Gaeta R, et al, Exploiting Petri nets to support fault tree based dependability analysis, Proceedings. The 8th International Workshop on Petri Nets and Performance Models, 1999, pp: 146-155
Boris Kovalerchuk, Evgenii Vityaev, Data Mining in Finance: Advances in Relational and Hybrid Methods, Kluwer, Dordrecht, 2000
Boyd M.A, Iverson D.L, Digraphs and fault trees: a tale of two combinatorial modeling methods, Reliability and Maintainability Symposium, Proceedings,Annual, 1993, pp: 220-226
Brockmann W, Comparison of fuzzy, rule-based and conventional process control.Proc. IFAC/IFIP/IMACS Symposium on Artificial Intelligence in Real Time Control-AIRTC'94, Valencia, 1994, pp: 117-122,
Caines P.E, Wang S, COCOLOG.. a conditional observer and controller logic for finite machines , Proceedings of the 29th IEEE Conference on Decision and Control, 1990.12.5—7, 5: 2845—2850
Castro J.L, How many rules are necessary to get a "good" fuzzy controller for a control problem? 1997, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, 1997.7.1-5, 2:749-754
Chan C.C, Batur C, Srinivasan A, Determination of quantization intervals in rule based model for dynamic systems, 1991 IEEE International Conference on Systems, Man, and Cybernetics, 1991'Decision Aiding for Complex Systems,Conference Proceedings, 1991, 3:1719-1723
Chan Chieno-Chung, A rough set approach to attribute generalization in data mining, Journal of InformationSciences, 1998, 107:169-176
Changchien S.Wesley, Lu Tzu-Chuen, Mining association rules procedure to support on-line recommendation by customers and products fragmentation,Expert Systems with Applications 2001.5, 20 (4): 325-335
Chen W.C, Chang Ni-Bin, Chen Jeng-Chung, Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment, Water Research,2003.1, 37 (1): 95-107
Chen Xiang-hui, Zhu Shan-jun, Ji Yin-dong, Entropy based uncertainty measures for classification rules with inconsistency tolerance, IEEE International Conference on Systems, Man, and Cybemetics, 2000, 4:2816-2821
Chen Yi-Qiang, Gao wen, Zhu Ting-shao, et.al, Learning prosodic patterns for mandarin speech synthesis, Journal of Intelligent Information Systems, 2002,19 (1) : 95-109
Cheung D.W, Han Jia-wei, Ng V.T, et al, Maintenance of discovered association rules in large databases: an incremental updating technique, 1996. Proceedings of the Twelfth International Conference on Data Engineering, 1996, pp: 106-114
Chi S, Lee S, Park S, Automated generation of fault tree using the symbolic DEVS simulation, AI, Simulation, and Planning in High Autonomy Systems, 1993.
'Integrating Virtual Reality and Model-Based Environments'. Proceedings.Fourth Annual Conference , 1993.9.20-22, pp: 148-154
Ching Torng Lin, Mao Jiun Wang, Hybrid fault tree analysis using fuzzy sets,Reliability Engineering and System Safety , 1997, 58:205-213
Cho Young-wan, Lee Kuchul, Yoo Jaehyung, et al, Autogeneration of fuzzy rules and membership functions for fuzzy modeling using rough set theory, LEE Proceedings-Control Theory and Applications, 1998.9, 145 (5): 437-442
Choubey S.K, Deogun J.S, Raghavan V.V, et al, A comparison of feature selection algorithms in the context of rough classifiers, 1996, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996.9.8-11,2:1122-1128
Chung Hong, Choi Kyung-Oak, Chung Hwan-Mook, Generation of approximation rules using information gain, Fuzzy Systems Conference Proceedings, 1999, 3:1241-1245
Coppit D, Sullivan K.J, Dugan J.B, Formal seananties of models for computational engineering: a case study on dynamic fault trees, 2000. Proceeding. 11th International Symposium on Software Reliability Engineering, 2000, pp: 270-282
Covadonga Fernandez-Baizan M, Menasalvas Ruiz E, Pena Sanehez J, et al,Using RSDM to mine socio-economic indicators, 1997. 'Computational Cybernetics and Simulation', 1997 IEEE International Conference on Systems, Man, and Cybernetics, 1997.10.12-15, 2:1385-1389
Dai Jian-Hua, Li Yuan-Xiang, Study on discretization based on rough set theory,Proceedings. 2002 International Conference on Machine Learning and Cybernetics, 2002, 3: 1371-1373
Dai Jian-Hua, Li Yuan-Xiang, Heuristic genetic algorithm for minimal redact in decision system based on rough set theory, Proceedings. 2002 Internafional Conference on Machine Learning and Cybernetics, 2002, 2:833-836
De Vries R.C, An automated methodology for generating a fault tree , IEEE Transactions on Reliability, 1990.4, 39 (1): 76-86
Devedzic V, A survey of modem knowledge modeling techniques, Expert Systems with Applications, 1999.tl, 17 (4): 275-294
Dong Hai-ying, Zhang Yu-bo, Xue Jun-yi, Hierarchical fault diagnosis for substation based on rough set , Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 4:2318-2321
Dugan J.B, Venkataraman B, Gulati R, DIFtree: a software package for the analysis
of dynamic fault tree models, Reliability and Maintainability Symposium. 1997 Proceedings, Annual, 1997.1.13-16, pp: 64-70
Dugan J.B, Sullivan K.J, Coppit D, Developing a low-cost high-quality software tool for dynamic fault-tree analysis, IEEE Transactions on Reliability, 2000.3,49 (1): 49-59
Düntsch Ivo, Gediga Günther, Uncertainty measures of rough set prediction,Artificial Intelligence, 1998.11, 106 (1): 109-137
Elliott M.S, Computer-assisted fault-tree construction using a knowledge-based approach, IEEE Transactions on Reliability, 1994.4, 43 (1): 112-120
Felix R, Ushio T, Rule induction from inconsistent and incomplete data using rough sets, 1999 IEEE International Conference on Systems, Man, and Cybernetics,1999, 5: 154-158
Felix R, Ushio T, Rough sets-based machine learning using a binary discernibility matrix, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999, 1 : 299-305
Filev D, Rule-base guided adaptation for mode detection in process control, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th,2001.7.25-28, 2:1068-1073
Filippetti F, Franceschini G, Tassoni C, et al, Recent developments of induction motor drives fault diagnosis using AI techniques, IEEE Transactions on Industrial Electronics, 2000.10, 47 (5): 994-1004
Fogarty T.C, Learning control rules, 1989. Proceedings. ICCON'89. IEEE International Conference on Control and Applications, 1989, pp: 900-906
Frank P.M, Analytical and qualitative model-based fault diagnosis-A survey and some new results. In: European Journal of Control, 1996,2 (1): 6-23
Freeman R.L, Grzymala-Busse J.W, Riffel L.A, et al, Analyzing the relation between heart rate, problem behavior, and environmental events using data mining system LERS, CBMS 2001. Proceedings. 14th IEEE Symposium on Computer-Based Medical Systems, 2001, pp: 11-16
Fu Li-min, Incremental knowledge acquisition in supervised learning networks, IEEE Transactions on Systems, Man and Cybernetics, 1996.11, Part A, 26 (6):801-809
Fujimori S, Kaiya T, Inoue T, Analysis of discharge currents with discernibility matrices, Proceedings of 1998 International Symposium on Electrical Insulating
Materials, 1998, pp: 649-652
Gang Zhao, Kobayashi A, Sakai Y, Image information retrieval using rough set theory, Processing. 2002. Proceedings. 2002 International Conference on Image, 2002, 1 : 417-420
Geymayr J.A.B, Ebecken N.F.F, Fault-tree analysis: a knowledge-engineering approach, IEEE Transactions on Reliability, 1995.3, 44 (1): 37-45
Gmytrasiewicz P, Hassberger J.A, Lee J.C, Fault tree based diagnostics using fuzzy logic, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990.11,12 (11): 1115-1119
Golan R.H, Ziarko W, A methodology for stock market analysis utilizing rough set theory, Computational Intelligence for Financial Engineering, Proceedings of the IEEE/IAFE, 1995, pp: 32-40
Greco Salvatore, Matarazzo Benedetto, Slowinski Roman, Rough sets theory for multicriteria decision analysis, European Journal of Operational Research,2001.2.15, 129 (1): 1-47
Gu X.P, Tso S.K, Zhang Q, Combination of rough set theory and artificial neural networks for transient stability assessment, International Conference on Power System Technology, 2000, Proceedings, PowerCon, 2000, 1:19-24
Guan J.W, Bell D.A, Rough computational methods for information systems,Artificial Intelligence, 1998, 105:77-103
Guan J.W, Bell D.A, Guan Z, Matrix computation for information systems,Information Sciences, 2001.1, 131 (1): 129-156
Guth M.A.S, A probabilistic foundation for vagueness and imprecision in fault-tree analysis, IEEE Transactions on Reliability. 1991.12, 40 (5): 563-571
Han Bin, Wu Tie-Jun, Data mining in multisensor system based on rough set theory, American Control Conference, Proceedings of the 2001, 2001, 6:4427-4431
Hannu Salonena, Hannu Nurmi, A note on rough sets and common knowledge events, European Journal of Operational Research, 1999, 112:692-695
Harry R.Lewis, Christos H.Papadimitriou, Elements of the Theory of Computation,PrenticeHatl, Inc 1999
Hashemi R.R, Le Blanc L.A, Rucks C.T, et al, A hybrid intelligent system for predicting bank holding structures, European Joumal of Operational Research,1998.9.1, 109 (2): 390-402
Haspel D, The role of rule based control in the process industry, IEE Colloquium on
Exploiting the Knowledge Base: Applications of Rule Based Control, 1989.7.1-3
Hirotaka Nakayama, Yuichi Hattori, Renichi Ishii, Rule extraction based on Rough Set thory and its application to medical data analysis, Systems, Man, and Cybernetics, 1999, IEEE SMC'99 Conference Proceedings, 1999, 5:924-929
Hong Tzung-pei, Tseng Li-huei, Wang Shyue-liang, Learning rules from incomplete training examples by rough sets, Expert Systems with Applications,2002.5, 22 (4): 285-293
Hong T.P, Wang T.T, Wang S.L, et al, Learning a coverage set of maximally general fuzzy rules by rough sets, Expert Systems with Applications, 2000, 19:97-103
Hor Ching-lai, Crossley P, Dunand F, Knowledge extraction within distribution substation using rough set approach, Power Engineering Society Winter Meeting, 2002. IEEE, 2002, 1: 654-659
Hu Tao, Lu Bing-Chao, Chen Guang-ju, A rotary machinery fault diagnosis approach based on rough set theory, 2000. Proceedings of the 3rd World Congress on Intelligent Control and Automation, 2000, 1:685-689.
Hu Xiao-hua, Cercone N, Mining knowledge rules from databases: a rough set approach, Proceedings of the Twelfth International Conference on Data Engineering, 1996, pp: 96-105
Hu Xiao-hua, Cercone N, Learning maximal generalized decision rules via discretization, generalization and rough set feature selection, 1997. Proceedings, Ninth IEEE International Conference on Tools with Artificial Intelligence, 1997.11.3-8, pp: 548-556
Hu Xiao-hua, Knowledge discovery in databases: an attribute—oriented rough set approach, Ph.D dissertation, Regina University 1995
Huan Liu, Feature selection via discretization, IEEE Transaction on knowledge and data engineering, 1997.7-8, 9 (4) : 642-645
Huan Liu, Setiono R, Chi2: feature selection and discretization of numeric attributes, Seventh International Conference on Tools with Artificial Intelligence, 1995. Proceedings, 1995, pp: 388-391
Huang P, Li Z, The measure of effect in an expert system for automatic generation of fault trees , IEEE Transactions on Reliability, 1992.3, 41 (1): 57-62
Huang Chihpin, Liu Chi-Bing, Automatic control for chemical dosing in laboratory-scale coagulation process by using an optical monitor, Water
Research, 1996.8,30 (8): 1924-1929
Hunt K.J, induction of decision trees for rule-based modelling and control,Proceedings of the 1992 IEEE International Symposium on Intelligent Control,1992.8.11-13, pp: 306-311
Jagielska Ilona, Matthews Chris, Whitfort Tim, An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems, Neurocomputing,1999.2, 24 (1-3): 37-54
Jaroslaw Stepaniuk, Optimizations of rough set model, Fundamenta Informaticae,1998, 36 (2-3): 265-283
Kasabov N.K, Qun Song, DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Transactions on Fuzzy Systems, 2002.4. 10 (2): 144-154
Katzberg J.D, Katzberg P, Predicting factors affecting likelihoods in engineering problems, 1999 IEEE Canadian Conference on Electrical and Computer Engineering, 1999, 2:1046-1051
Khan Faisal I, Abbasi S.A, Analytical simulation and PROFAT Ⅱ: a new methodology and a computer automated tool for fault tree analysis in chemical process industries, Journal of Hazardous Materials, 2000.7.10, 75 (1): 1-27
Kilyoo Kim, Sang-Hoon Han, A fast method of finding minimal outsets in a risk Monitor, Reliability Engineering and System Safety, 1998, 62:17-21
King-Kwok Ng, Wai Lam, Updating of association rules dynamically, Proceedings. 1999 International Symposium on Database Applications in Non-Traditional Environments, 2000, pp: 84-91
King P.J, Burnham K.J, James D.J.G, A combined rule-based and model-based adaptive control scheme, Control'94. International Conference on Control,1994.3.21-24, 2: 1484-1489
Kneale J.B, Application of expert system techniques to fault tree diagnostic data,1997 IEEE Autotestcon Proceedings, 1997, pp: 100-104
KomorowskiJ, PawlakZ, Polkowski L, etal, Rough Sets: ATutorial, technical report, 1999.
Kryszkiewicz Marzena, Rules in incomplete information systems, Information Sciences, 1999.2, 113 (3-4): 271-292
Kryszkiewicz Marzena, Rough set approach to incomplete information systems,
Information Sciences, 1998 (112), 39-49
Kuzawinski K.M, Smurthwaite R, Automated fault tree analysis via AI/ES,Reliability and Maintainability Symposium, 1988. Proceedings, Annual, 1988.1,pp: 331-335
Lambert-Tortes G, da Silva A.P.A, Quintana V.H, et al, Classification of power system operation point using rough set techniques, 1996, IEEE International Conference on Systems, Man, and Cybernetics, 1996.10.14-17, 3:1898-1903
Lan Shu, Mo Zhi-Wen, Hu Dan, Methods of leaming rules based on rough set:LBR and LEM3, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001, 2:753-756
Lee Seungkoo, Vachtsevanos George, An application of rough set theory to defect detection of automotive glass, Mathematics and Computers in Simulation,2002.9.30, 60 (3-5): 225-231
Li De-yu, Ma Yi-chen, Invariant characters of information systems under some homomorphisms, Information Sciences, 2000, 129:211-220
Li Long-Shu, Tang Bin, Ni Zhi-Wei, et al, A study on spatial attribute data mining based on rough set, 2002.Proceedings.2002 International Conference on Machine Learning and Cybernetics, 2002, 2:743-746
Li Xiao-lei, Wu Xiao-bing , The application of rough set theory in vehicle transmission system fault diagnosis, Vehicle Electronics Conference, 1999. (IVEC'99) Proceedings of the IEEE International, 1999, 1: 240-242
Liang Ji-ye, Xu Zong-ben, Uncertainty measures of roughness of knowledge and rough sets in incomplete information systems, Proceeding of the 3rd World Congress on Intelligent Control and Automation, 2000.6.28--7.2, Hefei, P.R.China
Liggesmeyer P, Rothfelder M, Improving system reliability with automatic fault tree generation, Digest of Papers, Twenty-Eighth Annual Intemational Symposium on Fault-Tolerant Computing, 1998, pp: 90-99
Lingras P, Rough set clustering for Web mining, 2002. FUZZ-IEEE'02.Proceedings of the 2002 IEEE International Conference on Fuzzy Systems,2002, 2:1039-1044
Liu Shu-lin, Shi Wen-gang, Rough set based intelligence diagnostic system for valves in reciprocating pumps, 2001 IEEE International Conference on Systems, Man, and Cybernetics, 2001, 1:353-358
Luzeaux D, Process control and machine learning: rule-based incremental control, IEEE Transactions on Automatic Control, 1994.6, 39(6):1166—1171
Luzeaux D, Let's learn to control a system! 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on Systems, Man and Cybernetics, 1993.10.17-20, 3:199-204
Luzeaux D, Incremental rule-based control and learning, Proceedings of the Third Annual Conference of AI, Simulation and Planning in High Autonomy Syslems, 1992. Integrating Perception, Planning and Action, pp: 23-28
Mackinson Steven, An adaptive fuzzy expert system for predicting structure, dynamics and distribution of herring shoals, Ecological Modeling, 2000.2.28, 126(2-3): 155-178
Maddouri M, Elloumi S, Jaoua A, An incremental learning system for imprecise and uncertain knowledge discovery, Information Sciences, 1998.8, 109(1-4):149-164
Mak Brenda, Munakata Toshinori, Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3, European Journal of Operational Research 2002.1.1, 136(1): 212-229
Malcolm Beynon, Reducts within the variable Precision rough sets model: A further investigation, European Journal of Operational Research, 2001, 134: 592-605
Maudal O, Preprocessing Data for Neural Network Based Classifiers-Rough Sets Vs Principal Component Analysis, MSc Thesis, University of Edinburgh, 1996
McCluskey E.G, Thompson S, Rule-based adaptive PID control using a pattern recognition approach, IEE Colloquium on Expert Systems in Process Control, 1988.3.28
McKee Thomas E, Lensberg Terje, Genetic programming and rough sets: A hybrid approach to bankruptcy classification, European Journal of Operational Research, 2002.4.16, 138 (2): 436-451
Miao Duo-qian, Wang Jue, Information-based algorithm for reduction of knowledge, 1997, ICIPS 97, 1997, IEEE International Conference on Intelligent Processing Systems, 1997, 2:1155-1158
Michael Doumpos, Constantin Zopounidis, Rough sets and multivariate statistical classification: A simulationstudy, computational economics, 2002, 19:287-301
Mikami D, Hagiwara M, Self-growing learning vector quantization with additional learning and rule extraction abilities, 2000 IEEE International Conference.on Systems, Man, and Cybernetics, 2000, 4: 2895-2900
Milne Robert, Drummond Mike, Renoux Patrick, Predicting paper making defects on-line using data mining, Knowledge-Based Systems, 1998.11.23, 11(5-6): 331-338
Mohammed Javeed Zaki, Scalable Data Mining for Rules, Ph.D Dissertation, University of Rochester Rochester, New York, 1998
Mrozek A, Plonka L, Kedziera J, The methodology of rough controller synthesis, 1996., Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996.9.8-11, 2: 1135-1139
Munakata T, Rough control: A perspective, in T.Y. Lin and N. Cercone(Eds.), Rough Sets and Data Mining: Analysis of Imprecise Data, Kluwer Academic, 1997, pp: 77-88.
Munakata T, Pawlak Z, Rough control: application of rough set theory to control, Proceedings, Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, 1996.9.2-5, 1:209-218
Nathalie Ch_ever, Franc_ois Gagn_e, et al, Application of rough sets analysis to identify pollutedaquatic sites based on a battery of biomarkers: a comparison with classical methods, Chemosphere, 2003, 51:13-23
Nguyen Hoang Phuong, Le Linh Phong, Santiprabhob P, et al, Approach to generating rules for expert systems using rough set theory, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001.7.25-28, 2:877-882
Nguyen H.S, Skowron A, Quantization of real values attributes, rough set and boolean reasoning approaches. Proc of the Second Joint Conference on Information Sciences, 1995.10, Wrightsville Beach, NC, pp:34-37.
Nguyen H S, Dscretization of real value attributes: A boolean reasoning approach, Warsaw: University of Poland, 1997
Nguyen H.S, Nguyen S.H, Discretization of Real Value Attributes for control problems, Workshop on Robotics, Inteligent Control, and Decision Support systems. PJWSTK, Warsaw, Poland, 1999
Nguyen S.H, Nguyen H.S, Some efficient algorithms for rough set methods, In Proceedings IPMU'96 Granada(Spain), pp. 1541-1457
Nils J Nilsson, Artificial Intelligence, A New Synthesis, China Machine Press,
1999
Nong Ye, The presentation of knowledge and state-Information for system fault diagnosis, IEEE transaction on Reliability, 1996.12, 45 (4): 638-645
Ou Lian, Sun Cai-xin, Bi Wei-min, et al, A steam turbine-generator vibration fault diagnosis method based on rough set, 2002. Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 3:1532-1534
Pal S.K, Mitra P, Multispectral image segmentation using the rough-set-initialized EM algorithm, IEEE Transactions on Geoscience and Remote Sensing, 2002.11, 40 (11):2495-2501
Pan HanSuk, Yun WonYoung, Fault tree analysis with fuzzy gates, Computers ind.Engng, 1997, 33(3-4): 569-572
Park Sang C, Piramuthu Selwyn, Shaw Michael J, Dynamic rule refinement in knowledge-based data mining systems, Decision Support Systems, 2001.6, 31(2):205-222
Pawlak Z, Rough set theory for intelligent industrial applications, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999, 1:37-44
Pawlak Z, Granularity of knowledge, Indiscemibility and rough sets, Fuzzy Systems Proceedings, 1998, IEEE World Congress on Computational Intelligence, 1998, 1:106-110
Pawlak Z, Why Rough Sets? Proc Of the fifth IEEE Inter conf on Fuzzy Systems, 1996, 2:738-743
Pawlak Z, Rough sets, decision algorithms and bayes' theorem, European journai of operational research, 2002, 136:181-189
Pawlak Z, Rough sets and intelligent data analysis, Information Sciences, 2002.11, 147(1-4): 1-12
Pawlak Z, Rough sets and data analysis, Fuzzy Systems Symposium, Soft Computing in Intelligent Systems and Information Processing, Proceedings of the 1996 Asian, 1-6
Pawlak Z, Rough set, Theoretical aspects of reasoning about data, Dordrecht. The Netherlands: KluwerAcademic Publishers, 1991
Pedrycz W, Granular computing: an introduction, IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, 2001, 3:1349-1354
Pedrycz W, Michael H Smith, Information granulation: Percepts and their stability, 2000, The Ninth IEEE International Conference on Fuzzy System, 2000, 1:
83-88
Pedrycz W, Han L, Peters J.F, Ramanna S, et al, Calibration of software quality: Fuzzy neural and rough neural computing approaches, Neurocomputing, 2001.2, 36 (1-4): 149-170
Peters J, Skowron A, Stepaniuk J, Information granules in spatial reasoning, IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, 2001, 3:1355-1360
Peters J.F, Ziaei K; Generating rules in selecting controller gains: a combined rough sets/fuzzy sets approach, IEEE Canadian Conference on Electrical and Computer Engineering, 1998, 1:233-236
Peters J.F, Skowron A, Suraj .Z, An application of rough set methods in control design, Fundamenta Informaticae, 2000, 43 (1-4): 269-290
Peters J.F, Pawlak Z, Skowron A, A rough set approach to measuring information granules, Computer Software and Applications Conference, 2002.Proceedings. 26th Annual International, 2002, pp: 1135-1139
Peters J.F, Ramanna S, Borkowski M, et al, Sensor fusion: a rough Petri net model, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001.7.25—28, 5:2882—2887
Peters L, Beck K, Camposano R, Fuzzy logic controller with dynamic rule set, Proceedings of the 1992 IEEE International Symposium on Intelligent Control, 1992, pp:216-219
Polkowsk L, Skowron A, Towards adaptive calculus of granules, Fuzzy Systems Proceedings, The 1998 IEEE International Conference on Computational Intelligence, 1998, 1:111-116,
Questier F, Arnaut-Rollier I, Walczak B, et al, Application of rough set theory to feature selection for unsupervised clustering, Chemometrics and Intelligent Laboratory Systems 2002.9.28, 63 (2): 155-167
Radzikowska Anna Maria, Kerre Etienne E, A comparative study of fuzzy rough sets, Fuzzy Sets and Systems, 2002.3.1, 126(2):137-155
Ramanna S, Peters J.F, Ahn T, Software quality knowledge discovery: a rough set approach, Computer Software and Applications Conference, 2002. Proceedings. 26th Annual International, 2002, pp: 1140-1145
Ren Yansong, Bechta Dugan J, Design of reliable systems using static and dynamic fault trees, IEEE Transactions on Reliability, 1998.9, 47(3):234-244
Rivals I, Personnaz L, Internal model control using neural networks, ISIE 96,
Proceedings of the IEEE International Symposium on Industrial Electronics, 1996.6.17—20, 1: 109—114
Roslyn M.Sinnamon, John D.Andrews, Fault Tree Analysis and Binary Decision Diagrams, 1996 Proceeding Annual Reliability and Maintainability Symposium, 1996, pp:215-222
Roy Amitava, Pal Sankar K, Fuzzy discretization of feature space for a rough set classifier, Pattern Recognition Letters, 2003.3, 24(6):895-902
Sarda N.L, Srinivas N.V, An adaptive algorithm for incremental mining of association rules, Proceedings. Ninth International Workshop on Database and Expert Systems Applications, 1998, pp:240-245
Seungkoo Lee, An Architecture for a Diagnostic/Prognostic System with Rough Set Feature Selection and Diagnostic Decision Fusion Capabilities, Ph.D dissertation, Georgia Institute of Technology, 2002
Shah N, Ziarko W, Data-based acquisition and incremental modification of classification rules, Computational Intelligence, 1995, 11:357-370
Shao J, Finding reducts with user specified criteria, 1997. Proceedings., Eighth International Workshop on Database and Expert Systems Applications, 1997.9.1-2, pp: 352-357
Shen Li-xiang, Francis E.H.Tay, et al, Fault Diagnosis using Rough Sets Theory, Computers in Industry, 2000, 43:61-72
Shen Q, Chouchoutas A, FuREAP: a Fuzzy-Rough Estimator of Algae Populations, Artificial Intelligence in Engineering, 2001.1, 15(1):13-24
Shen Qiang, Chouchoulas Alexios, A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems, Engineering Applications of Artificial Intelligence, 2000.6.1, 13(3):263-278
Shi Wen-gang, Wang Ri-xin, Huang Wen-hu, Application of rough set theory to fault diagnosis of check valves in reciprocating pumps, 2001.Canadian Conference on Electrical and Computer Engineering, 2001, 2:1247-1250
Shu Hongehun, Sun Xiangfei, Si Dajun, Study of fuzzy controller for voltage and reactive power in substation using rough set theory, 2002. Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 4:2452-2456
Shu Hong-chun, Sun Xiang-fei, Si dajun, A new method for locating faults on transmission lines based on rough set and FNN, 2002. Proceedings. PowerCon 2002. International Conference on Power System Technology, 2002, 4:2584
-2588
Sinnamon R.M, Andrews J.D, Fault tree analysis and binary decision diagrams, Reliability and Maintainability Symposium, 1996 Proceedings. 'International Symposium on Product Quality and Integrity', Annual, 1996.1.22-25, pp: 215-222
Skowron Andrzej, Polkowski Leeh, Rough mereological foundations for design, analysis, synthesis, and control in distributed systems, Information Sciences, 1998.1, 104(1-2):129-156
Slowinski R, Vanderpooten D, A generalized definition of rough approximations based on similarity, IEEE Transactions on Knowledge and Data Engineering, 2000.3-4, 12 (2): 331-336
Son H Nguyen, Skowron A, Boolean reasoning for feature extraction problems., Tenth International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'97), (1997) October15-18, Charlotte, NC, USA, Lecture Notes in Artificial Intelligence 1325, Springer-Verlag, Berlin, 1997, pp:117-126.
Sridhar D.V, Bartlett E.B, Seagrave R.C, An information theoretic approach for combining neural network process models, Neural Networks, 1999.7, 12 (6): 915-926
Sridhar D. V, Seagrave R. C, Bartlett E. B, Process modeling, using stacked neural networks. AIChE J, 1996, 42:2529.
Sugeno M, Tanaka K, A fuzzy-logic-based approach to qualitative modeling, IEEE Trans on Fuzzy Systems, 1993, 1(1):7-31
Sun Ying-kai, Cao Long-han, Cao Chang-xiu, Slab defect diagnosis and predicting system in CC processing based on rough set theory, 2000. Proceedings of the 3rd World Congress on Intelligent Control and Automation, 2000, 1:673-675
Susmaga Robert, Analyzing discretizations of continuous attributes given a monotonic discrimination function, Intelligent Data Analysis, 1997, 1 (1-4): 157-179
Swiniarski Roman W, Skowron Andrzej, Rough set methods in feature selection and recognition, Pattern Recognition Letters, 2003.3, 24 (6):833-849
Tan Tian-Le, Song Zhi-Huan, Li Ping, Matrix computation for data cleaning and rule extraction in knowledge System, Proceedings of 2002 International Conference on Machine Learning and Cybernetics, 2002, 1:116—120
Tay F.E.H, Shen Li-xing, A modified Chi2 algorithm for discretization, IEEE
Transactions on Knowledge and Data Engineering, 2002.5-6, 14 (3): 666-670
Tay F.E.H, Shen Li-xing, Economic and nancial prediction using rough sets model, European Journal of Operational Research, 2002, 141:641-659
Therasa Beaubouef, Frederick E Petry, Gurdial Arora, Information-theoretic measures of uncertainty for rough sets and rough relation databases, Journal of Information Sciences, 1998, 109:185-195
Tong Ling-yun; An Li-ping, Incremental learning of decision rules based on rough set theory, 2002. Proceedings of the 4th World Congress on Intelligent Control and Automation, 2002, 1: 420-425
Tsau Young, Lin T.Y, Fuzzy sets, rough set and probability, Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American, 2002, pp: 302—305
Tsumoto S, Statistical evidence for rough set analysis, 2002. FUZZ—IEEE'02. Proceedings of the 2002 IEEE International Conference on Fuzzy Systems,2002, 1: 757—762
Tsumoto S, Automated discovery of positive and negative knowledge in clinical databases, IEEE Engineering in Medicine and Biology Magazine, 2000.7-8,19 (4): 56-62
Tsumoto S, Rule induction system based on characterization of medical diagnosis using rough sets IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001, 4:2364-2369
Tsumoto S, Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic, Information Sciences, 2000, 124:125-137
Tsumoto S, Discovery of rules for medical expert systems-rough set approach,1999.JCCIMA '99. Proceedings. Third International Conference on Computational Intelligence and Multimedia Applications, t999, pp: 2t2-216
Tsumoto S, Tanaka H, Extraction of domain knowledge from databases based on rough set theory, 1996, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996.9.8—11, 2:748-754
Wang Guo-Yin, Zheng Zheng, Zhang Yi, Ridas-a rough set based intelligent data analysis system, 2002.Proceedings. 2002 International Conference on Machine Learning and Cybernetics, 2002, 2:646-649
Wang Yi-Fan, Mining stock price using fuzzy rough set system, Expert Systems with Applications, 2003.1, 24 (1): 13-23
Wang Zhi-liang, Meng Wen-bo, Gu Xue-jing, et al, The research of the policeGIS spatial data classification technology based on rough set,2002.Proceedings of the 4th World Congress on Intelligent Control and Automation,2002, 1:432-436
Way Yuan-Shun, Hsia Der-Yu, A simple component-connection method forbuilding binary decision diagrams encoding a fault tree, Reliability Engineeringand System Safety, 2000.10, 70 (1): 59-70
Wei Jin-mao, Huang Dao, Knowledge degeneration method based on rough set,Proceeding of 3~(rd) Asian Control Conference, 2000.7.4-7, Shanghai, pp: 2429-2434
Wen Hao, A knowledge discovery method and its industrial applications,Proceedings of the 3rd World Congress on Intelligent Control and Automation,2000, 1:369-372
Wolpert D.H, Stacked generalization, Neural Networks, 1992, 5 (2): 241.
Woon Y.K, Wee-Keong Ng, Das A, Fast online dynamic association rule mining,Proceedings of the Second International Conference on Web InformationSystems Engineering, 2001, 1: 278-287
Valentin N, Denoeux T, A neural network-based software sensor for coagulationcontrol in a water treatment plant, Intelligent Data Analysis, 2001, 5:23-39
Xu Xiu-ping, Peters J.F, Rough set methods in power system fault classification,2002. IEEE CCECE 2002. Canadian Conference on Electrical and ComputerEngineering, 2002, 1:100-105
Xu Z.B, Liang J.Y, Dang C.Y, et al., Inclusion degreee: a perspective on measuresfor rough set data analysis, Information sciences, 2002, 141:227-236
Yao Y, Rough sets, neighborhood systems and granular computing, 1999 IEEECanadian Conference on Electrical and Computer Engineering, 1999, 3:1553-1558
Yao Y.Y, Constructive and algebraic methods of the theory of rough sets,Information Sciences, 1998.8, 109 (1—4): 21—47
Yao Y.Y, Stratified rough sets and granular computing, Fuzzy InformationProcessing Society, 1999, NAFIPS, 18th International Conference of the NorthAmerican, 1999, pp: 800-804
Yao Y.Y, A comparative study of fuzzy sets and rough sets, Information Sciences,1998.8, 109 (1-4): 227-242
Yao Y.Y, Wong S.K.M, Generalized probabilistic rough set models, Fuzzy Systems
Symposium, 1996. 'Soft Computing in Intelligent Systems and InformationProcessing', Proceedings of the 1996 Asian, 1996.12.11-14, pp: 158-163
Yeung Yam, Nguyen H.T, Kreinovich V, Multi-resolution techniques in the rules-based intelligent control systems: a universal approximation result,Proceedings of the 1999 IEEE International Symposium on IntelligentControl/Intelligent Systems and Semiotics, 1999, pp: 213-218
Yoon J.P, Techniques for data and rule validation in knowledge based systems,Computer Assurance, COMPASS '89, Proceedings of the Fourth AnnualConference on Systems Integrity, Software Safety and Process Security,1989.6.19-23, pp: 62-70
Yuan B, Wang X.Z, Morris T, Software analyser design using data miningtechnology for toxicity prediction of aqueous effluents, Waste Management,2000.12, 20 (8): 677-686
Zhang Qi, Han Zhen-xiang, Wen Fu-shuan, A new approach for fault diagnosisin power systems based on rough set theory[A]. Fourth International Conferenceon Advances in Power System Control, Operation and Management, 1997, 2:597-602.
Zhang Qi, Han Zhen-xiang, Wen Fu-shuan, A new approach for fault diagnosisin power systems based on rough set theory, 1997. APSCOM-97. FourthInternational Conference on Advances in Power System Control, Operation andManagement (Conf. Publ. No. 450),1997.11.14, 2:597-602
Zhong Ning, Dong Ju-zhen, Ohsuga S, et al, An incremental, probabitistic roughset approach to rule discovery, Fuzzy Systems Proceedings, 1998, IEEE WorldCongress on Computational Intelligence, 1998, 2:933-938
Zhong Ning, Dong Ju-zhen, Ohsuga Setsuo, Rule discovery by soft inductiontechniquesm, Neurocomputing, 2001.2, 36 (1-4): 171-204
Zhong Ning; Dong Ju-zhen, Liu Chun-nian, et al, A hybrid model for rulediscovery in data, Knowledge-Based Systems, 2001.11.1, 14 (7): 397-412
安利平，刘喜华等，不相容决策系统中获取规则的粗糙集方法，青岛大学学报：工程技术版，2002，17(1)：53—55
安利平，仝凌云，粗糙集理论中一种属性离散化算法，河北工业大学学报，2002，31(3)：39—43
卞学海，非一致性数据的必然规则学习，华东船舶工业学院学报，1998，12(1)：25—30
白桦，李圭白，混凝投药的神经网络控制方法，给水排水，2001，27(11)：83
—86
毕建东，曲复宛，一个机器学习定量属性的定性方法TCIN，哈尔滨工业大学学报，1997．10，29(5)：73—76
蔡智，王煦法等，基于粗糙集理论的连续属性离散化算法研究，计算机科学，2001．5(专刊)，28(5)：39—41
曹龙汉，孙颖楷等：基于粗糙集理论的连铸坯缺陷诊断预报系统，重庆大学学报(自然科学版)，2001，24(1)：95—98
曹龙汉，曹长修，基于粗糙集理论的柴油机神经网络故障诊断研究，内燃机学报，2002，20(4)：357—361
曹长修，孙颖楷等，基于粗糙集理论的内燃机故障诊断专家系统，重庆大学学报(自然科学版)，2001，24(4)：45—47
陈秉正，韩春鹏，归纳式学习中连续型数据的区间划分问题，系统工程理论与实践，2001．4，4：1—17
陈德刚，张文修，粗糙集和拓扑空间，西安交通大学学报，2001，35(12)：1313—1315
陈海，粗糙集理论在内燃机柱塞故障诊断中的应用，贵州工业大学学报：自然科学版，2002，31(1)：46—49
陈湘晖，朱善君等，扩展的粗糙集模型及其不确定性量度，清华大学学报：自然科学版，2002，42(1)：128—131
程明华，姚一平，动态故障树分析方法在软、硬件容错计算机系统中的应用，航空学报，2000，11(1)：34—37
程岩，黄梯云，基于Rough Set理论发现最小归纳依赖关系的方法研究，计算机工程，2000，26(3)：26—27，4
杜海峰，丁国锋等，基于粗糙集与模糊神经网络的多级压缩机诊断，西安交通大学学报，2001，35(9)：940—944
范世栋，支天云，粗糙集的代数性质，山西大学学报：自然科学版，2001，24(2)：116—119
冯玉才，冯剑琳，关联规则的增量式更新算法，软件学报，1998．4，9(4)：301—306
冯纯伯，费树岷，非线性控制系统分析与设计(第2版)，北京，电子工业出版社，1998
高峰，谢剑英，发现关联规则的增量式更新算法，计算机工程，2000，26(12)：49—50，112
桂修文，郑尚龙等，知识库的一致性与完备性检验，华中理工大学学报：增刊(Ⅱ)，1991，19(A02)：33—39
杭小树，熊范纶，基于粗糙集理论的示例学习研究，浙江大学学报：理学版，2002，29(3)：346—354
韩斌，基于数据挖掘的信息融合理论和应用，浙江大学博士学位论文，杭州，2002
韩祯祥，张琦，粗糙集理论及其应用综述，控制理论与应用，1999，16(2)：153—157
韩祯祥，张琦，粗糙集理论及其应用，信息与控制，1998，27(1)：37—45
郝丽娜，徐心和，粗糙集理论在智能故障诊断中的应用，中国机械工程，2002，13(21)：1856—1858
郝丽娜，徐心和，基于粗糙集理论的规则修正方法，控制与决策，2002，17(5)：614—616，620
洪立，王世渊，王先来，神经网络控制器在未建模型的非线性系统中的应用，自动化与仪器仪表，2000．3，pp：4—6
缑锦，叶东毅，基于概念格的求所有绝对属性约简的一个算法，福州大学学报：自然科学版，2002，30(3)：298—300
侯利娟，王国胤等，粗糙集理论中的离散化问题，计算机科学，2000，27(12)：89—94
候忠生，非参数模型及其自适应控制理论，北京，科学出版社，1999
胡静，曹先彬等，基于相容粗糙集的图形图像信息预检索，计算机辅助设计与图形学学报，2002，14(3)：242—246
胡涛，吕炳朝等，一种基于粗糙集理论的燃气轮发电机组故障诊断新方法，电子测量与仪器学报，2001．3，15(1)：11—16
胡涛，吕炳朝等，基于粗糙集的不确定知识表示方法，计算机科学，2000，27(3)：90—93
胡寿松，自动控制原理，北京，国防工业出版社，1994
胡可云，陆玉昌等，粗糙集理论及其应用进展，清华大学学报：自科版，2001，41(1)：64—68
胡可云，陆玉昌等，基于概念格的分类和关联规则的集成挖掘方法，软件学报，2000，11(11)：1478—1484
黄冬梅，高印芝等，模糊决策树归纳算法及应用，河北师范大学学报：自然科学版，1999．6，23(2)：173—176
黄沛，李剑，基于粗糙集的保险风险规则挖掘模型，系统工程，2002，20(5)．34—39
黄曾麟，粗集理论及其应用，重庆大学出版社，重庆，1998
黄文虎，纪常伟等，基于故障树模型的智能诊断的确定性推理，机械强度，
1995．5，17(2)：38—42
贾要勤，徐光华等，粗糙集属性约简的基因算法，中国机械工程，2000．7，11(7)：797—800
廖志伟，张雅明等，基于数据挖掘模型的配电网故障定位诊断，天津大学学报：自然科学与工程技术版，2002，35(3)：322—326
黎明，张化光，基于粗糙集的神经网络建模方法研究，自动化学报，2002，28(1)．—27—33
厉玉鸣，瓮维勤，非线性控制系统，化学工业出版社，北京，1985
李龙澍，程慧霞等，基于凸Rough集的数据约简和规则发现研究，东南大学学报：自然科学版，2002，32(2)：201—205
李玉榕，乔斌等，基于熵的粗糙集属性简约算法，电路与系统学报，2002，7(3)：8—12
李永敏，朱善君，根据粗糙集理论进行BP网络设计的研究，系统工程理论与实践，1999，19(4)：62—69
李文，孙辉，陈善本，一种建立模糊模型的粗糙集方法，控制理论与应用，2001．2，18(1)：69—75
刘健勤，粗糙集理论及其最新进展，计算技术与自动化，1998．3，17(1)：43—48
刘宜平，沈毅等，设备故障诊断的粗糙集方法研究，高技术通讯，2001，5：77—79
刘宜平，沈毅等：一种基于粗糙集理论的容错网络实现及其在故障诊断中的应用，电机与控制学报，2000，4(2)：117—121
刘业政，杨善林等，粗糙集数据分析的计算方法，合肥工业大学学报：自然科学版，2002，25(2)：161—166
刘振华，刘三阳，基于粗糙集的采煤方式选择模型，计算机与现代化，2002．9，85(9)：1—3
刘震宇，郭宝龙等，一种新的用于连续值属性离散化的约简算法，控制与决策，2002，17(5)：545—549
刘宗田，属性最小约简的增量式算法，电子学报，1999，27(11)：96—98
刘清，Rough集及Rough推理，科学出版社，2001
卢荣德，陈宗海等，思维模拟在复杂工业过程建模中应用的综述，信息与控制，2002，31(4)：346—352
梁吉业，孟晓伟，信息熵在粗糙集理论中的应用，山西大学学报：自然科学版，2002，25(3)：281—284
粱吉业，徐宗本等，包含度与粗糙集数据分析中的度量，计算机学报，2001．5，
24(5)：544—547
马志峰，邢汉承等，决策表中规则获取的不确定行研究，控制与决策，2000，15(6)：703—707
马志锋，刑汉承等，粗糙控制中的规则获取策略研究，工业仪表与自动化装置，2000．6，PP：58—61
马元元，孙志挥等，时态数据库中增量关联规则的挖掘，计算机研究与发展，2000．12，37(12)：1446—1451
苗夺谦，范世栋，知识的粒度计算及其应用，系统工程理论与实践，2002， 22(1)：48—56
苗夺谦，Rough Set理论中连续属性的离散化方法，自动化学报，2001．5，27(3)：296—302
苗夺谦，胡桂蓉，知识约简的一种启发式算法，计算机研究与发展，1999．6，36(6)：681—684
苗夺谦，王珏，粗糙集理论中概念与运算的信息表示，软件学报，1999，10(2)：113—116
苗夺谦，王珏，粗糙集理论中知识粗糙性与信息熵关系的讨论，模式识别与人工智能，1998，11(1)：34—40
苗夺谦，王珏，基于粗糙集的多变量决策树构造方法，软件学报，1997．6，8(6)：425—431
权光日，刘文远等，连续属性空间上的规则学习算法，软件学报，1999：11，10(11)：1225—1232
乔斌，郑洪涛等，针对信息系统不完备性的粗糙集分层递阶约简，电路与系统学报，2001．6，2(2)：78—82
石冰，郑燕峰，改进型关联规则增量式更新算法与实现，小型微型计算机系统，2000，21(12)：1327—1329
石云，孙玉芳，基于Rough Set的空间数据分类方法，软件学报，2000,11(5)：673—678
史忠植，知识发现，清华大学出版社，2002，1
史定华，王松瑞，故障树分析技术方法和理论，北京，北京师范大学出版社，1993．11
苏健，高济，基于元信息的粗糙集规则增式生成方法，模式识别与人工智能，2001．12，14(4)：428—433
孙颖楷，张邦礼等，基于粗糙集理论的人工神经网络故障诊断系统，重庆大学学报：自然科学版，2000，23(6)：53—55
余彬海，周祖德，面向故障树结构的不精确知识管理，华中理工大学学报，1995，
23(2)：27—29
束洪春，司大军等，电力变压器故障诊断专家系统知识库建立和维护的粗糙集方法，中国电机工程学报，2002，22(2)：31—35
陶兰，唐玉荣，时态数据库周期规律与关联规则的挖掘，中国农业大学学报，2001．6，4：76—80
唐建国，粗糙集理论决策推理时样本缺损的处理方法，重庆三峡学院学报，2000，16(6)：78—80
谭天乐，宋执环，李平，信息系统数据清洗、规则提取的矩阵算法，信息与控制，2003．4，
谭天乐，宋执环，李平，基于粗糙集的故障诊断方法，浙江大学学报：工学版，2003．1，37(1)：47—50
吴今培，肖健华，智能故障诊断与专家系统，北京，科学出版社，1997
汪德澍，黄明瑞，非线性控制系统引论，成都，成都电讯工程学院出版社，1988
王珏，王任等，基于Rough Set理论的“数据浓缩”，计算机学报，1998．5，21(5)：393—400
王海清，工业过程监测：基于小波和统计学的方法，浙江大学博士学位论文，杭州，2000
王卫平，李熙亚等，基于粗糙集的数控机床故障诊断最小化方法，组合机床与自动化加工技术，2001，pp：31—34
王骥程，过程动态模型，杭州，浙江大学出版社，1994
王君圣，李敏强等，粗糙集中决策规则集的综合评判生成方法，第三届国际智能控制与自动化会议，中国安徽，合肥，2000，6．28—7．2
王国胤，Rough集理论与知识获取，西安交通大学出版社，2001。
王继承，朱明富，VDM算法在数据采掘中的应用，华中科技大学学报，2001．6，增刊，pp：90—92
王清毅，蔡智等，部分数据缺失环境下的知识发现方法，软件学报，2001，12(10)：1516—1524
王彬，熵与信息，西北工业大学出版社，西安，1994，10
王翔，袁兆山，多层次关联规则的增量式更新算法，合肥工业大学学报：自科版，2000，23(2)：200—204
王志海，胡可云，基于粗糙集合理论的知识发现综述，模式识别与人工智能，1998，11(2)：176—183
许志兴，伍华林等，基于信息熵的连续属性自动聚类算法，南京航空航天大学学报，2001．6，33(3)：233—236
夏雨佳，李少远等，一种基于粗糙集的信息系统决策规则提取方法，控制与决
策，2001，16(5)：577—580
谢志鹏，刘宗田，概念格与关联规则发现，计算机研究与发展，2000，37(12)：1415—1421
谢克明，杨静，粗糙集理论及其在智能控制领域的应用前景，大连理工大学学报，1999．7，30(4)：338—342
谢克明，侯宏仑，复杂系统的智能控制方法，太原理工大学学报，1998，29(6)：568—572，576
许志兴，陆金桂等，一种基于粗糙集理论的粗糙神经网络构造方法，南京航空航天大学学报，2001，33(4)：355—359
徐一新，叶东毅，知识约简的差别矩阵启发式算法，福州大学学报，2000．6，28(3)：120—123
徐章遂，最大故障信息量二元树诊断法，无线电通信技术，1989，15(2)：28—38
肖勇，陈意云，用遗传算法构造决策树，计算机研究与发展，1998．1，35(1)：49—52
顼晓娟，谢克明，基于粗糙集神经网络PID的串级控制系统设计，太原理工大学学报，2002，33(6)：610—612
叶东毅，基于粗糙隶属函数的规则组合可信度计算与推理，福州大学学报：自然科学版，2002，30(3)：294—297叶东毅，信息表相对属性约简的一个算法，福州大学学报：自然科学版，2000．12，28(6)：7—9
叶东毅，陈昭炯，一个改进的粗糙集属性约简算法，福州大学学报：自然科学版，2002，28(5)：9—12
袁小宏，赵仲生等，粗糙集理论在机械故障诊断中的应用研究，西安交通大学学报，2001，35(9)：954—958
于金龙，李晓红等，连续属性值的整体离散化，哈尔滨工业大学学报，2000．6，32(3)：48—53
杨会志，于春田，一种浮选过程控制的新策略—粗糙控制，矿业研究与开发，1999．10，19(5)：20—23
虞和济，候广琳，故障诊断的专家系统，北京，冶金工业出版社，1991
张飞弓，叶东毅，粗糙集连续属性量化算法的灵敏度分析，福州大学学报：自科版，2000，28(3)：28—30
张继勇，陆继远等，基于粗集方法的模糊控制器的优化设计，电气自动化，2002，24(2)：7—9
张文宇，薛惠锋等，粗糙集在数据挖掘分类规则中的应用研究，西北工业大学学报，2002，20(3)：430—434
张文修，吴伟志等，粗糙集理论与方法，北京，科学出版社，2001
张学民，安利平，动态数据库规则获取和更新的粗糙集方法，天津大学学报，2001．7，34(4)：529—532
张琦，韩祯祥等，一种基于粗糙集理论的电力系统故障诊断和警报处理新方法，中国电力，1998，31(4)：32—38
周东华，叶银忠，现代故障诊断与容错控制，清华大学出版出版社，2000．6
周经纶，孙权，一种故障树分析的新算法，模糊系统与数学，1997，11(3)：74—78
周青龙，故障诊断与监控，北京，兵器工业出版社，1992
周育健，王珏，RSL：基于Rough Set的表示语言，软件学报，1997，8(8)：569—576
邹先霞，杜威等，一种基于粗集理论的概念格构造方法，华中师范大学学报：自科版，2001，35(2)：146—149
赵卫东，盛昭瀚等，粗糙集在决策树生成中的应用，东南大学学报：自然科学版，2000．7，30(4)：132—137
朱玉，王先来，对未建模的工业过程对象进行控制的新方法，自动化与仪器仪表，2002．3，pp：35—39