粗糙集理论及粗糙混合智能方法在船舶电力系统中的应用研究
|
摘要
目前，船舶控制向着自动化和智能化的方向发展，发电机系统的容量不断增大，船舶电力系统越来越复杂，对船舶电力系统的研究也提出了更高的要求。采用新的人工智能技术、方法研究船舶电力系统，进行智能化信息处理、动态建模和智能控制，并应用于船舶电力系统是有现实意义的。
波兰数学家Z.Pawlak于1982年提出的粗糙集理论(Rough Set Theory)是一种较新的软计算方法，它能有效地分析和处理不精确、不一致、不完整等各种不完备信息，并从中发现隐含的知识，揭示潜在的规律，在某些方面可以弥补其它软计算方法的不足。粗糙集理论的发展以及粗糙集与神经网络的集成研究为船舶电站的建模与控制提供了一种新的思路和方法。目前，粗糙集理论在船舶电站电力系统中的研究和应用还较少，尚处在起步、探索阶段。本文利用粗糙集理论在智能信息处理方面的突出特点、强大的数据分析能力，以及神经网络的自组织学习、容错性能，对粗糙集理论以及粗糙集和RBF神经网络混合系统在船舶电力系统建模与控制中的应用进行探索研究。
主要研究内容包括：
●粗糙集理论算法研究
针对粗糙集方法只能处理量化数据以及传统离散化方法存在的缺点，提出了一种基于微粒群优化(PSO)的连续属性离散化方法，降低了连续属性离散化后决策表的不相容度；在深入研究粗糙集理论的基础上，发现了正区域的一些有用性质，提出了一种基于正区域的直接求核方法，并给出了两个利用正区域求取属性相对约简的算法；为了在属性约简的过程中省去对属性正区域的多次计算，引入了广义决策表这一工具，发现了一些度量属性核与相对约简的性质，提出了一种基于广义决策表的属性求核与约简算法，可以非常简单地求取属性的核与相对约简，所给算法既适用于相容决策表也适用于不相容决策表；由于从实际系统中采集到的数据所构成的信息系统往往是不完备的，通常要对其进行预处理，提出了一种不完备信息系统的属性相对约简算法；决策规则的约简是基于粗糙集智能信息处理的一个重要内容，基于二进制可辨矩阵给出一个简单的直接求取决策规则核的方法，并提出一种决策规则的约简算法。
●基于粗糙集的径向基函数神经网络(粗糙-RBF网络)
粗糙集理论与神经网络都是智能信息处理较为有效的方法，但都有着各自的局限性，同时两者之间存在着许多互补之处，为两者的集成研究提供了理论基础。在深入分析径向基函数特性和网络结构的基础上，提出了一种基于粗糙集理论的RBF神经网络(粗糙-RBF网络)设计方法，利用粗糙集简化神经网络输入样本，确定RBF网络的中心向量候选集和扩展常数，结合正交最小二乘(OLS)算法构造RBF网络的结构。
●基于粗糙-RBF网络的船舶发电机动态建模方法
粗糙集理论和神经网络的集成运用为处理包含不确定、不完整信息的复杂系统提供了一个强有力的工具，也为复杂系统的建模提供了一种新的思路。本文在对粗糙集理论与RBF神经网络集成研究的基础上，针对现有复杂非线性系统神经网络建模存在的缺点与不足，提出了一种基于粗糙-RBF网络的连续系统动态建模方法，并利用这种方法对具有复杂动态特性和不确定性的船舶同步发电机进行建模。
●基于粗糙混合智能方法的船舶发电机励磁控制
粗糙控制是近年来兴起的一种新的智能控制方法，粗糙集理论处理不确定性问题的独特方式以及它与其它理论较好的融合性将有利于它在智能控制领域的进一步发展。尽管对粗糙控制已经进行了一些研究，但数量和所占据的地位仍然相对较小，并且都是学术上的而不是真正意义上的应用。在计算智能领域，通过各种理论与方法相结合的方式解决实际问题是目前的主要研究方向之一。本文探索了基于粗糙集理论的混合智能算法，并应用于船舶同步发电机的励磁控制系统中。针对船舶电力系统的特点，首次提出了基于粗糙-RBF网络辨识的发电机励磁神经PID控制和粗糙-神经网络逆前馈补偿的发电机励磁复合控制两种混合智能粗糙控制方法，仿真结果验证了所设计方法的有效性。
At present, automatic and intelligent control is the important developing direction for modern ships. The electrical capacity of ship generators is growing large, which impels ship power system more and more complex. So the demand for the ship power system research is becoming much higher. It will have the practical significance that adopting the new artificial intelligence technologies and methods to realize information process, dynamic modeling and intelligent control in the ships power system.
Rough set theory putted forward by Polish scientist Z. Pawlak is a new soft computing method. It can deal with imprecise, uncertain and incomplete data validly, find the underlying knowledge or rules from initial information system, and can overcome weaknesses of other soft computing method in some aspects. The development of rough set theory and the research of integrating rough set with neural networks provide a new way for ship power station modeling and control. Up to now, the research and application of rough set theory in ship power system are very rare. In this dissertation, according to the data analysis ability of rough set theory in intelligent information processing, and the self-organizing and fault-tolerance capability of neural networks, the dynamic modeling and control for ship power system based on the hybrid intelligent method of rough set theory and RBF neural networks are researched mainly. The major innovations in this article are as follows:
Rough Set Theory and Algorithms
It is a pity that rough set theory can only deal with the discrete attributes. Aiming at the weaknesses of traditional discretization methods, an algorithm for continuous attributes discretization based on particle swarm optimization (PSO) is presented here, which can reduce the incompatibility degree of discretized decision table. The rough set theory is deeply investigated, and some useful properties of the positive region are discovered. A method for attribute core computation directly based on the positive region is proposed, and then, two algorithms for attribute relative reduction based on positive region are given. In order to leave out the repeated computation of positive region in the attribute reduction process, a kind of generalized information table is introduced; on the basis of it a criterion of attribute core and relative reduction is provided. A method for calculating attribute core is presented. And then, an algorithm for relative attribute reduction based on the
generalized information table is designed, which is suitable for not only consistent decision table but also inconsistent decision table. In real applications, many information systems are incomplete because of different reasons, and need pretreatment. An algorithm for calculating attribute relative reduction in incomplete systems directly is put forward. Moreover, the decision rules reduction is an important topic in the research on intelligent information processing based on rough set theory. A method for calculating decision rules core based on binary discernibility matrix directly is presented. And then, an algorithm for decision rules reduction is designed.
RBF Neural Networks Constructing based on Rough Set Theory (RS-RBF Neural Networks)
Rough set theory and neural networks, which are both valid methods for intelligent information processing, have respective limitations; meanwhile there are fine complementarities between them, which provide the theoretical foundation for their integration research. After analyzing the characteristics of radial basis function and the structure of RBF neural networks deeply, a RBF neural network configuring method based on rough set theory, namely RS-RBF networks, is presented. Firstly, in order to build RBF neuron center vectors candidate set and spreads, the training samples are reduced based on rough set theory. Then, orthogonal least squares method is used to construct RBF networks.
Ship Synchronous Generator Dynamic Modeling Method Based on RS-RBF Neural Networks
The integration of rough set theory and neural networks supplies a powerful way for information processing of complex nonlinear system with uncertain and incomplete data, and also provides a new approach for complex nonlinear system modeling. By virtue of respective advantage about rough set theory and RBF networks as well as their complementarities, the integration of them was researched, and a dynamic modeling method based on RS-RBF neural networks is presented. The method was applied to model the ship synchronous generator with complex dynamic characteristics and uncertainties. The simulation results prove the validity of this method.
Ship Generator Excitation Control based on Hybrid Intelligent Rough Control Methods
Rough control is a newly arisen intelligent control method in recent years. It is good for rough set theory development in intelligent control field that the particular way of dealing with uncertain problems and the good properties of fusion with other uncertain theories. Although there have been some researches on rough control, their number and domains are relatively small, and they are all academic rather than real life applications. In the field of computational intelligence, it is one of the most promising ways that synthesizing different theories to solve the practical problems. In this dissertation, hybrid intelligent methods based on rough set theory were pilot studied for excitation control system of ship synchronous generator. Aiming at the characteristics of ship power system, two hybrid intelligent rough control methods were presented for the first time. The one is an adaptive neural PID control strategy based on RS-RBF neural networks identification; another one is excitation compound control method with rough neural inverse system feed forward compensation. The simulation results prove the validity of these methods.
波兰数学家Z.Pawlak于1982年提出的粗糙集理论(Rough Set Theory)是一种较新的软计算方法，它能有效地分析和处理不精确、不一致、不完整等各种不完备信息，并从中发现隐含的知识，揭示潜在的规律，在某些方面可以弥补其它软计算方法的不足。粗糙集理论的发展以及粗糙集与神经网络的集成研究为船舶电站的建模与控制提供了一种新的思路和方法。目前，粗糙集理论在船舶电站电力系统中的研究和应用还较少，尚处在起步、探索阶段。本文利用粗糙集理论在智能信息处理方面的突出特点、强大的数据分析能力，以及神经网络的自组织学习、容错性能，对粗糙集理论以及粗糙集和RBF神经网络混合系统在船舶电力系统建模与控制中的应用进行探索研究。
主要研究内容包括：
●粗糙集理论算法研究
针对粗糙集方法只能处理量化数据以及传统离散化方法存在的缺点，提出了一种基于微粒群优化(PSO)的连续属性离散化方法，降低了连续属性离散化后决策表的不相容度；在深入研究粗糙集理论的基础上，发现了正区域的一些有用性质，提出了一种基于正区域的直接求核方法，并给出了两个利用正区域求取属性相对约简的算法；为了在属性约简的过程中省去对属性正区域的多次计算，引入了广义决策表这一工具，发现了一些度量属性核与相对约简的性质，提出了一种基于广义决策表的属性求核与约简算法，可以非常简单地求取属性的核与相对约简，所给算法既适用于相容决策表也适用于不相容决策表；由于从实际系统中采集到的数据所构成的信息系统往往是不完备的，通常要对其进行预处理，提出了一种不完备信息系统的属性相对约简算法；决策规则的约简是基于粗糙集智能信息处理的一个重要内容，基于二进制可辨矩阵给出一个简单的直接求取决策规则核的方法，并提出一种决策规则的约简算法。
●基于粗糙集的径向基函数神经网络(粗糙-RBF网络)
粗糙集理论与神经网络都是智能信息处理较为有效的方法，但都有着各自的局限性，同时两者之间存在着许多互补之处，为两者的集成研究提供了理论基础。在深入分析径向基函数特性和网络结构的基础上，提出了一种基于粗糙集理论的RBF神经网络(粗糙-RBF网络)设计方法，利用粗糙集简化神经网络输入样本，确定RBF网络的中心向量候选集和扩展常数，结合正交最小二乘(OLS)算法构造RBF网络的结构。
●基于粗糙-RBF网络的船舶发电机动态建模方法
粗糙集理论和神经网络的集成运用为处理包含不确定、不完整信息的复杂系统提供了一个强有力的工具，也为复杂系统的建模提供了一种新的思路。本文在对粗糙集理论与RBF神经网络集成研究的基础上，针对现有复杂非线性系统神经网络建模存在的缺点与不足，提出了一种基于粗糙-RBF网络的连续系统动态建模方法，并利用这种方法对具有复杂动态特性和不确定性的船舶同步发电机进行建模。
●基于粗糙混合智能方法的船舶发电机励磁控制
粗糙控制是近年来兴起的一种新的智能控制方法，粗糙集理论处理不确定性问题的独特方式以及它与其它理论较好的融合性将有利于它在智能控制领域的进一步发展。尽管对粗糙控制已经进行了一些研究，但数量和所占据的地位仍然相对较小，并且都是学术上的而不是真正意义上的应用。在计算智能领域，通过各种理论与方法相结合的方式解决实际问题是目前的主要研究方向之一。本文探索了基于粗糙集理论的混合智能算法，并应用于船舶同步发电机的励磁控制系统中。针对船舶电力系统的特点，首次提出了基于粗糙-RBF网络辨识的发电机励磁神经PID控制和粗糙-神经网络逆前馈补偿的发电机励磁复合控制两种混合智能粗糙控制方法，仿真结果验证了所设计方法的有效性。
At present, automatic and intelligent control is the important developing direction for modern ships. The electrical capacity of ship generators is growing large, which impels ship power system more and more complex. So the demand for the ship power system research is becoming much higher. It will have the practical significance that adopting the new artificial intelligence technologies and methods to realize information process, dynamic modeling and intelligent control in the ships power system.
Rough set theory putted forward by Polish scientist Z. Pawlak is a new soft computing method. It can deal with imprecise, uncertain and incomplete data validly, find the underlying knowledge or rules from initial information system, and can overcome weaknesses of other soft computing method in some aspects. The development of rough set theory and the research of integrating rough set with neural networks provide a new way for ship power station modeling and control. Up to now, the research and application of rough set theory in ship power system are very rare. In this dissertation, according to the data analysis ability of rough set theory in intelligent information processing, and the self-organizing and fault-tolerance capability of neural networks, the dynamic modeling and control for ship power system based on the hybrid intelligent method of rough set theory and RBF neural networks are researched mainly. The major innovations in this article are as follows:
Rough Set Theory and Algorithms
It is a pity that rough set theory can only deal with the discrete attributes. Aiming at the weaknesses of traditional discretization methods, an algorithm for continuous attributes discretization based on particle swarm optimization (PSO) is presented here, which can reduce the incompatibility degree of discretized decision table. The rough set theory is deeply investigated, and some useful properties of the positive region are discovered. A method for attribute core computation directly based on the positive region is proposed, and then, two algorithms for attribute relative reduction based on positive region are given. In order to leave out the repeated computation of positive region in the attribute reduction process, a kind of generalized information table is introduced; on the basis of it a criterion of attribute core and relative reduction is provided. A method for calculating attribute core is presented. And then, an algorithm for relative attribute reduction based on the
generalized information table is designed, which is suitable for not only consistent decision table but also inconsistent decision table. In real applications, many information systems are incomplete because of different reasons, and need pretreatment. An algorithm for calculating attribute relative reduction in incomplete systems directly is put forward. Moreover, the decision rules reduction is an important topic in the research on intelligent information processing based on rough set theory. A method for calculating decision rules core based on binary discernibility matrix directly is presented. And then, an algorithm for decision rules reduction is designed.
RBF Neural Networks Constructing based on Rough Set Theory (RS-RBF Neural Networks)
Rough set theory and neural networks, which are both valid methods for intelligent information processing, have respective limitations; meanwhile there are fine complementarities between them, which provide the theoretical foundation for their integration research. After analyzing the characteristics of radial basis function and the structure of RBF neural networks deeply, a RBF neural network configuring method based on rough set theory, namely RS-RBF networks, is presented. Firstly, in order to build RBF neuron center vectors candidate set and spreads, the training samples are reduced based on rough set theory. Then, orthogonal least squares method is used to construct RBF networks.
Ship Synchronous Generator Dynamic Modeling Method Based on RS-RBF Neural Networks
The integration of rough set theory and neural networks supplies a powerful way for information processing of complex nonlinear system with uncertain and incomplete data, and also provides a new approach for complex nonlinear system modeling. By virtue of respective advantage about rough set theory and RBF networks as well as their complementarities, the integration of them was researched, and a dynamic modeling method based on RS-RBF neural networks is presented. The method was applied to model the ship synchronous generator with complex dynamic characteristics and uncertainties. The simulation results prove the validity of this method.
Ship Generator Excitation Control based on Hybrid Intelligent Rough Control Methods
Rough control is a newly arisen intelligent control method in recent years. It is good for rough set theory development in intelligent control field that the particular way of dealing with uncertain problems and the good properties of fusion with other uncertain theories. Although there have been some researches on rough control, their number and domains are relatively small, and they are all academic rather than real life applications. In the field of computational intelligence, it is one of the most promising ways that synthesizing different theories to solve the practical problems. In this dissertation, hybrid intelligent methods based on rough set theory were pilot studied for excitation control system of ship synchronous generator. Aiming at the characteristics of ship power system, two hybrid intelligent rough control methods were presented for the first time. The one is an adaptive neural PID control strategy based on RS-RBF neural networks identification; another one is excitation compound control method with rough neural inverse system feed forward compensation. The simulation results prove the validity of these methods.
引文
1. Pawlak Z. Rough sets. International Journal of Computer and Information Sciences, 1982.11:341～356
2. Pawlak, Z. Rough Sets and Intelligent Data Analysis. Information Sciences, 2002, 147: 1～12
3. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Puhlishers. 1991
4. Pawlak, Z. Some Issues on Rough Sets. In: Peters, J.F. et al Eds.: Transactions on Rough Sets I. Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg New York 2004, LNCS 3100:1～58
5．王国胤．Rough集理论与知识获取[M]．西安：西安交通大学出版社，2001
6．张文修 等 编著．粗糙集理论与方法[M]．北京：科学出版社，2001
7．吴明芬．粗糙集理论的研究现状与前景[J]．五邑大学学报(自然科学版)，2002，16(2)：16～21
8. Munakata, T., Pawlak, Z. Rough Control: Application of Rough Set Theory to Control. Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, September 2～5,1996, volume 1:209～218
9．张文修，吴伟志．粗糙集理论介绍和研究综述[J]．模糊系统与数学，2000，14(4)：1～12
10. Lambert-Tomes G, et al. Data Mining into a Control Center Database via Rough Set Techniques. In: Proc. of the International Conference on Intelligent System Application to Power Systems (ISAP' 97). Seoul (Korea), 1997, 246～250
11．束洪春，孙向飞，于继来．粗糙集理论在电力系统中的应用[J]．电力系统自动化，2004，28(3)：90～95
12．张琦，韩祯祥，文福拴．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[J]．中国电力，1998，31(4)：32～35
13．束洪春，孙向飞，司大军．基于粗糙集理论的配电网故障诊断研究[J]．中国电机工程学报，2001，21(10)：73～78
14．张琦，韩祯祥等．用于暂态稳定评估的人工神经网络输人空间压缩方法[J]．电力系统自动化，2001，25(2)：32～39
15. Liu ShuLin, Shi Wengang. Rough Set Based Intelligence Diagnostic System for Valves in Reciprocating Pumps. In: IEEE International Conference on Systems, Man, and Cybernetics, 2001, volume 1:353～358
16. KHOO L. P., Tor S. B. Li J. R. A Rough Set Approach to the Ordering of Basic Events in a Fault Tree for Fault Diagnosis. Advanced manufacturing technology, 2001, 17(10): 769～774
17. Ou Jian, Sun Caixin, Bi Weimin, et al. A steam turbine-generator vibration fault diagnosis method based on rough set. In: Proceedings of IEEE International Conference on Power System Technology, 2002, volume 3:1532～1534
18. Shu Hongchun, Sun Xiangfei, Si Dajun. A new method for locating faults on transmission lines based on rough set and FNN. In: Proceedings of IEEE International Conference on Power System Technology, 2002, volume 4:2584～2588
19. Cao L, Cao C, Guo Z, et al. The research of fault diagnosis for fuel injection system of diesel engine with ANN based on rough sets theory. In: Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, 2002:410～414
20. Francis E.H. Tay, Shen Lixiang. Fault diagnosis based on Rough Set Theory. Engineering Applications of Artificial Intelligence, 2003, 16:39～43
21. Wang QingHui, Li Jing Rong. A rough set-based fault ranking prototype system for fault diagnosis. Engineering Applications of Artificial Intelligence, 2004, 17:909～917
22. Li Ru-qiang, Chen Jin, et al. Fault diagnosis of rotating machinery based on SVD, FCM and RST. Int J Adv Manuf Technol, 2005
23. Caccavale F., Villani L. Fault Diagnosis for Industrial Robots. Fault Diagnosis and Fault Tolerance for Mechatronic Systems, STAR 1, 2003:85～108
24. Tsumoto S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Information Sciences, 2004, 162:65～80
25. Zhang Q, Han Z, et al. A new approach for fault diagnosis in power systems based on rough set theory. Proceedings of the 4th International Conference on Advances in Power System Control, Operation and Management, 1997, 2:597～602
26. Wakulicz-Deja A, Paszek P. Diagnose progressive encephalopathy applying the rough set theory. International journal of medical informatics, 1997, 46:119～127
27．张腾飞,王锡淮，等．粗糙集理论在故障诊断中的应用综述[J]．上海海事大学学报，2005，26(4)：20～25
28．张琦，韩祯祥，曹绍杰，等．用于暂态稳定评估的人工神经网络输人空间压缩方法[J]．电力系统自动化，2001，25(2)：32～39
29. Gu X P, Tso S K. Application Rough Set Concept to Neural-network-based Transient Stability Classification of Power Systems. In: Proceeding of the 5th International Conference on Advance in Power System Control, Operation and Management, Hong Kong:IEE.2000:400～404
30．顾雪平，张文朝．基于Tabu搜索技术的暂态稳定分类神经网络的输人特征选择[J]。 中国电机工程学报，2002，22(7)：66～70
31．张庆宝，程浩忠，等．基于粗糙集属性约简算法和支持向量机的短期负荷预测[J]．电网技术，2006，30(8)：56～59
32．冯丽，邱家驹．粗糙集理论在短期电力负荷预测中的应用[J]．华北电力技术，2004(4)：5～7
33．钟波，周家启，肖智．基于粗糙集与神经网络的电力负荷新型预测模型[J]．系统工程理论与实践，2004，24(6)：113～119
34．谢宏，程浩忠，等．基于粗糙集理论建立短期电力负荷神经网络预测模型[J]．中国电机工程学报，2003，23(11)：1～4
35. Xiao J. M., Zhang T.F., Wang X.H. Ship power load prediction based on RST and RBF neural networks. In: J. Wang, X. Liao, and Z. Yi (Eds.): Advances in Neural Networks. Lecture Notes in Computer Science, LNCS 3498. Springer-Verlag, Berlin Heidelberg New York, 2005:648～653
36．赵卫东，陈国华．粗集与神经网络的集成技术研究[J]．系统工程与电子技术，2002，24(10)：103～107
37. Ohrn A. Rough Logic Control: a new approach to automatic control?[J].[Project Report] Norwegian University of Science and Technology, Trondheim, Norway, 1993
38. Mrozek, A., Plonka, L., Kedziera J. The Methodology of Rough Controller Synthesis[J]. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996, 2(9): 1135～1139
39．杨会志，于春田．一种浮选过程控制的新策略——粗糙控制[J]．矿业研究与开发，1999，19(5)：20～23
40．谢克明，杨静．粗糙集理论及其在智能控制领域的应用前景[J]．太原理工大学学报，1999，30(4)：338～342
41．杨国豪 编著．船舶电站控制装置及动态模拟系统[M]．大连：大连海事大学出版社，1999
42．黄伦坤，朱正鹏，刘宗德编．船舶电站及其自动装置[M]．人民交通出版社，1981
43．陆让之，张声钺，黄春兰编．船舶电机学[M]．人民交通出版社，1986
44．陈宗海 编著．过程系统建模与仿真[M]．合肥：中国科学技术大学出版社，1997
45．黄曼磊，李殿璞，唐嘉亨．船舶电站柴油机调速系统H_∞控制器的设计[J]。中国造船，2001(4)：46～51
46．袁季修．电力系统安全稳定控制[M]．北京：中国电力出版社，1996
47．[苏]．柯瓦连科著．钱瑞麟译．船舶同步发电机自动励磁调节和稳定性[M]．国防工业出版社，1980
48．卢强，王仲鸿，韩英铎．输电系统最优控制[M]．北京：科学出版社，1982
49．卢强，孙元章．电力系统非线性控制[M]．北京：科学出版社，1993
50. Sun C, Zhao Z. Design of Nonlinear Robust Excitation Control for Multi-Machine Power System. IEE Proc. Gener. Transm. Distrib. 1996, 143(3): 253～257
51. Samarasing V. G. D. C, Pahalawaththa N.C. Design of a Robust Variable Structure Controller for Improving Power System Dynamic Stability. International Journal of Electrical Power & Energy Systems, 1996, 18(8): 519～525
52．刘清．Rough集及Rough推理[M]．北京：科学出版社，2001
53. Skowron A, et al. The discernibility matrices and functions in information system[A]. In: Slowinski R. et al, Intelligent Decision Support-Handbook of Applications and Advances of the rough set theory[C]. Kluwer Academic Publishers. 1992:331～363
54．王东锴，梁樑．含冲突的决策表中的决策规则发现[J]．系统工程，2003，21(1)：8～11
55．王国胤，于洪，杨大春．基于条件信息熵的决策表约简[J]．计算机学报，2002，25(7)：759～766
56．王国胤．Rough集理论代数与信息论观点的关系研究[J]．世界科技研究与发展，2002，24(5)：20～26
57. Kryszkiewics M. Rough set approach to incomplete information system[J]. Information science, 1998, 112:39～49
58．林泰崴，陈昭炯，叶东毅．不完备信息系统下限制容差关系模型的改进[J]．福州大学学报(自然科学版)，2004，vol．32 supp．：39～42
59. Stefanowski J, Tsoukias A. On the Extension of Rough Sets under Incomplete Information. In: Zhong N, et al, Eds. Proc of the 7th Int. Workshop on New Directions in Rough, Data Mining, and Granular-soft Computer. Berlin:Springer-Verleg, 1999, 73～81
60．王国胤．Rough集理论在不完备信息系统中的扩充[J]．计算机研究与发展，2002，39(10)：1238～1243
61．丛爽著．神经网络、模糊系统及其在运动控制中的应用[M]．合肥：中国科学技术大学出版社，2001．5
62．徐丽娜 编著．神经网络控制[M]．北京：电子工业出版社，2003．2
63．魏海坤 编著．神经网络结构设计的理论与方法[M]．北京：国防工业出版社，2005
64. Jianmei Xiao. Application of RBF Neural Network to Freeway Traffic Flow Modeling. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, P.R. China, 2004:5208～5211
65. Chen S. Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks. IEEE Transactions on Neural Networks, 1999, 10(5): 1239～1243
66．赵清林，郭艳兵，梅强等．确定RBF神经网络中心点的方法综述[J]．广东自动化与信息工程，2002(2)：13～15
67. http://www.dmresearch.net/html/content/paper-on-data-mining/1000002482.php
68．高隽 编著．智能信息处理方法导论[M]．机械工业出版社，2004．6
69．候利娟 等．粗糙集理论中的离散化问题[J]．计算机科学，2000，27(12)：89～94
70. Nguyen H S, Skowron A. Quantization of real value attributes. Proceeding of Second Joint Annual Conference on Information Science, Wrightwille Beach, North Carolina, 1995:34～37
71. Xiang Xin-jian, Stolle.M. An algorithm of discretization of continuous attributes in rough sets based on cluster. Journal of Zhejiang University of Science and Technology, 2003, 15(3): 154～157
72. Amitava Roy, Sankar K.Pal. Fuzzy discretization of feature space for a rough set classifer. Pattern Recognition Letters, 2003, 24:895～902
73．张文宇，贾嵘，薛惠锋等．知识发现过程中连续属性离散化方法研究[J]．西安理工大学学报，2002，18(3)：249～253
74．苗夺谦．Rough Set理论中连续属性的离散化方法[J]．自动化学报．2001，27(3)：296～302
75. Eberhart R. C, Kennedy J. Particles Swarm Optimization. IEEE International Conference on Neural Network, Perth, Australia, 1995:1942～1948
76. Kennedy J, Eberhart R. C. Swarm Intelligence.San Francisco, CA:Morgan Kaufmann, 2001
77. Zwe-Lee Gaing. Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints, IEEE Transactions on Power Systems, August 2003, 18(3): 1187～1195
78. Pawlak Z. Rough sets theory and its applications to data nanlysis[J]. Cybernetics and Systems, 1998, 29: 661～668
79．刘少辉等．Rough集高效算法的研究[J]．计算机学报，2003，26(5)：524～529
80. Hu X H, Cercone N. Learning in relational databases: A rough set approach. Computational Intelligence, 1995, 11(2): 323～337
81．叶东毅，陈昭炯．一个新的差别矩阵及其求核方法[J]．电子学报，2002，30(7)：1086～1088
82．王国胤．决策表核属性的计算方法[J]．计算机学报．2003，5，26(5)：611～615
83．徐一新，叶东毅．知识约简的差别矩阵启发式算法[J]．福州大学学报(自然科学版)，2000，28(3)：120～123
84．刘文军等．基于可辨识矩阵和逻辑运算的属性约简算法的改进[J]．模式识别与人工智能，2004，17(1)：119-123
85. Mi J. et al. Approaches to knowledge reduction based on variable precision rough set model[J]. Information Sciences, 2004, 159(3): 255～272
86. Zhang M. et al. A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory[J]. Expert Systems, 2003, 20(5): 298～304
87. Wong S K M, Ziarko W. On Optimal Decision Rules in Decision Ttables. Bulletin of Polish Academy of Science: 1985,33(12): 693～696
88. http://www.ics.uci.edu/～mlearn/MLRePOSitory.html
89．刘文军等．一种求粗糙集中最小属性约简的新算法[J]．北京师范大学学报(自然科学版)，2004，40(1)：8～12
90. Jelonek J, Krawiec K, et al. Rough set reduction of attributes and their domains for neural networks. International Journal of Computational Intelligence, 1995, 11(2): 339～347
91．苗夺谦，胡桂荣．知识约简的一种启发式算法[J]．计算研究与发展，1999，36(6)：681～684
92．张腾飞，肖健梅，王锡淮．粗糙集理论中属性相对约简算法[J]．电子学报，2005，33(11)：2080～2083
93．何伟等．不完备信息系统下的属性约简算法[J]．计算机科学，2004，31(2)：117～119
94. Liang, Jiye, Xu, Zongben. The algorithm on knowledge reduction in incomplete information systems[J]. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2002, 10(1): 95～103
95. Qiao, Bin, Zhang, Guohong, Jiang, Jingping. Hierarchical reduction approach of rough sets to incomplete systems[A]. IEEE Region 10th Annual International Conference, Proceedings/TENCON[C], 2002, vol.1: 1～4
96. Sun, Hui-Qin, Xiong, Zhang. Finding minimal reducts from incomplete information systems[A]. International Conference on Machine Learning and Cybernetics[C], 2003, vol.1: 350～354
97．王珏，苗夺谦，周育健．关于Rough Set理论与应用的综述[J]．模式识别与人工智能，1996，9(4)：337～344
98. Yang Yanyi, Chiam T. C. Rule discovery based on rough set theory. 12th International Symposium on Integrated Ferroelectrics, Aachen, Germany, 2000:11～16
99．刘文军等．基于区分矩阵求决策算法的约简[J]．北京师范大学学报(自然科学版)，2003，39(3)：311～315
100. Fleix R, Ushio T. Rough Sets-based Machine Learning Using a Binary Discernibinity Matrix[J]. IPMM, 1999:299～305
101．支云天，苗夺谦．二进制可辨矩阵的变换及高效属性约简算法的构造[J]．计算机科学，2002，29(2)：140～142
102．叶东毅．一个新的二进制可辨识矩阵及其核的计算[J]．小型微型计算机系统，2004，25(6)：965～967
103. Xihuai, Wang, Tengfei, Zhang, etc. A New Algorithm for Relative Attribute Reduction in Decision Table, The 6th World Congress on Intelligent Control and Automation, Dalian, P.R. China, 2006:4051～4054
104．施伟锋，郑华耀．船舶自动化电站系统仿真[J]．系统仿真学报，2003，15(9)：1249～1252
105．黄曼磊，李殿璞．船舶电站同步发电机调压系统的数学模型[J]．哈尔滨工程大学学报，2004，25(3)：305～308
106．Fitzgerald AE，Kingsley，C等著．刘新正，苏少平，高琳 等译．电机学[J]．北京：电子工业出版社，2004
107. ONG, Chee-Mun. Dynamic Simulation of Electric Machinery: Using MATLAB/Simulink. Prentice Hall, 1998
108．吴天明 等编著．MATLAB电力系统设计与分析[M]．北京：国防工业出版社，2004
109．黎明，张化光．基于粗糙集的神经网络建模方法研究[J]．自动化学报，2002，28(1)：27～33
110．刘璨，陈统坚，彭永红等．基于粗集理论的模糊神经网络建模方法研究[J]．中国机械工程，2001，12(11)：1256～1259
111. Qin Zhongguang, Mao Zongyuan. A New Algorithm for Neural Network Architecture Study. Proceedings of 3th World Congress on Intelligent Control and Automation, Heifei, P. R. China, 2000: 795～799
112．陈遵德．Rough Set神经网络智能系统及其应用[J]．模式识别与人工智能，1999，12(1)：1～5
113. Li Qiu-dan, Chi Zhong-xian, Shi Wen-bing. Application of Rough Set Theory and Artificial Neural Network for Load Forecasting. Proceedings of the first International Conference on Machine Leaning and Cybernetics, Beijing, 2002: 1148～1152
114. 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 Application, 2000, 18:65～74
115. Son Nguyen H, Szczuka M, Slezak D. Neural Network Design: Rough Set Approach to Real-valued Data. Komorowski J, Ztkow J Eds, The First European Symposium on Principles of Data Mining and Knowledge Discovery. Berlin: Trondheim, Norway, Springer-Verlag, 1997:359～366
116. Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design[M]. PWS Publishing Company, 1996
117．徐晓霞，陈涛，王晓升．基于正交最小二乘算法的小波神经网络[J]．电子学报，1998，26(10)：115～117
118. Micchelli C. Interpolation of scattered data: distance matrices and conditionally positive definite functions. Constructive Approximation, 1986(2): 11～22
119. Wang, Xi, Zhang, T., Xiao, J.: Ship Synchronous Generator Modeling Based on RST and RBF Neural Networks. In: Wang, J. et al. (Eds.): Advances in Neural Networks. Lecture Notes in Compute Science, Vol. 3972, Springer-Verlag, Berlin Heidelberg New York, 2006:1363～1369
120．施亿生．船舶电站[M]．北京：国防工业出版社，1983
121．施伟锋，陈子顺，汤天浩．船舶发电机励磁系统的CMAC神经网络并行控制[J]．电网技术，2005，29(4)：31～35．
122．黄曼磊．船舶电站同步发电机H_∞调压器的仿真研究[J]．哈尔滨工程大学学报，2004，25(2)：162～166
123. Lu Qiang, Sun Yuan-zhang, Mei Shengwei. Nonlinear control systems and power system dynamics[M]. Boston: Kluwer Academic Publishers, 2001
124．唐巍，郭镇明，李殿璞．混沌优化用于电力系统静态负荷模型参数辨识[J]．电力系统自动化，2000，24(13)：27～30
125．刘金琨 著．先进PID控制及其MATLAB仿真[M]．北京：电子工业出版社，2003
126．厉启宏，宋华屏．船舶交流发电机调压器差动横向电流补偿工作原理分析[J]．上海造船，2004，1：61～65
127. Dai J. J., Xiao Di, et al. Emergency Generator Startup Study of a Hydro Turbine Unit for a Nuclear Generation Facility[J]. IEEE Trans. on Industry Applications, 2004, 40(5): 1191～1999
128. Mark J. L. Orr. Matlab Functions for Radial Basis Function Networks, Technical report, Institute for Adaptive and Neural Computation, Division of Informatics, Edinburgh University, 1999
129．戴先中．多变量非线性系统的神经网络逆控制方法[M]．北京：科学出版社，2005
130．张腾．神经网络逆系统方法及其在电力系统控制中的应用[学位论文]．南京：东南大学，2002
131．陈双叶，易继锴．基于粗糙集理论的模糊模型及其在复杂控制系统中的应用[J]。 控制与决策，2002，vol．17，suppl：747～751
132．谭天乐．基于粗糙集的过程建模、控制与故障诊断[学位论文]．杭州：浙江大学，2003
133．郝丽娜，徐心和．基于粗糙集理论的规则修正方法[J]．控制与决策，2002，17(5)：614～616
134．唐肝翌，邓辉文，张栒．规则不完备的模糊系统新规则在线生成[J]．计算机工程与应用，2006，63～65
135．叶东毅．基于粗糙隶属函数的规则组合可信度计算与推理[J]．福州大学学报(自然科学版)，2002，30(3)：294～297
136．王秀，叶东毅．基于分布约简的获取规则的增量式方法[J]．福州大学学报(自然科学版)，2005，33(1)：16～19
137．王加成，王庆东等．粗糙集理论中规则和综合决策的可信度计算的新方法[J]．信息与控制，2003，32(7)：724～728
138. Tan Tian-Le, Song Zhi-Huan, Li Ping. Rough set based modeling and controller design in an internal model control system[J]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2003, volume 4:3559～3564
139．徐建英，刘贺平．永磁同步电动机参考模型逆线性二次型最优电流控制调速系统[J]．中国电机工程学报，2007，27(15)：21～27
140．宋夫华，李平．基于支持向量机a阶逆系统方法的非线性内模控制[J]．自动化学报，2007，33(7)：778～781
141．程仁洪．基于近似逆系统的异步电机复合控制方法[J]．电机与控制学报，2001，5(3)：163～165
142．史伟伟，唐国庆，李俊．动态电压恢复器的双前馈控制策略研究[J]，电力自动化设备，2007，27(2)：11～15
143．王永林，王杰．神经网络前馈逆控制在交流伺服系统中的应用[J]．郑州大学学报(自然科学版)，2000，32(3)：51～56
144．曲东才，何友．一种BPMFN逆模型补偿的复合控制结构方案研究[J]．宇航学报，2006，27(6)：1414～1418
145．董立新，肖登明等．基于油中溶解气体分析数据挖掘的变压器绝缘故障诊断[J]．电力系统自动化，2004，28(15)：85～89
146．白如江．基于粗糙集和RBF神经网络的文本自动分类方法[J]．现代图书情报技术，2006，vol．6：47～51
147．孟科，张恒喜等．基于粗糙集的RBF神经网络在装备研制费用预测中的应用[J]．装备指挥技术学院学报，2006，17(5)：14～17
148. Wu Di, Zhang Yaping, Wang Xin. RBF neural network for hierarchical intrusion detection systems based on RS feature selection[J]. Journal of Computational Information Systems, 2007, 3(4): 1335～1342
149. Zhang Xu, Sun Jianbo, Guo Chen. A novel method of intelligent fault diagnosis for diesel engine[A]. Proc. of the 6th Word Congress on Intelligent Control and Automation[C], vol.2: 5739～5743
150. Liu Hongjie, Feng Boqin, Xia Kewen. Seismic reservoir oil-gas prediction study based on rough set and RBF network[A]. Proc. of the 6th World Congress on Intelligent Control and Automation[C], vol. 1:4229～4233
151．李昌彪，夏克文等．一种基于属性重要性的粗糙RBF神经网络[J]．控制与决策，2006，21(7)：821～824
152．何明，冯博琴等．一种基于粗糙集的粗糙神经网络构造方法[J]．西安交通大学学报，2004，38(12)：1240～1242
153. Sienkiewicz J Rough set and rough function approaches to the control algorithm reconstruction. Institute of Computer Science Reports, Warsaw University of Technology, Warsaw, 1996
154. Mrozek A. Rough sets in computer implementation of rule-based control of industrial process. In: R. Slowinski (Ed.), Intelligent Decision Support: Handbook of applications and advances of the Rough sets theory. Kluwer academic publishers, Boston, 1992:19～31
155. Pawlak Z. Rough real functions and rough controllers. In: Lin T.Y, Cercone N. (Eds.), Rough Sets and Data. Mining. Analysis of Imprecise Data, Kluwer Academic Publishers, Boston 1997:139～147
156. Mrozek A. Use of rough sets and decision tables for implementing rule-based control of industrial Process. Bulletin of the Polish Academy of Sciences: Technical Sciences, 1986, 34(5-6): 357～371
157. Czogala E, Mrozek A, Pawlak Z. The idea of a rough fuzzy controller and its application to the stabilization of a pendulum-car system[J]. Fuzzy Sets and Systems, 1995, 72:61～73
158. Plonka L and Mrozek A. Rule-based stabilization of the inverted pendulum[J]. Computational Intelligence, 1995, 11(2): 348～356
159. Peters J. F., Ziarko K, Ramanna S. Approximate Time Rough Control: Concepts and Application to Satellite Attitude Control, Proc. of RSCTC'98, Warsaw, Poland, 1998: 491～498
160. Peters J. F., Ziaei K, Generating rules in selecting controller gains: a combined rough set/fuzzy sets approach, IEEE Canadian Conference on Electrical and Computer Engineering, 1998, 1:233～236
161. Tseng C H, Lin T Y, Chi C W. Adaptive aggregation of modular control. Proc. IEEE fuzzy sets and systems, 1996: 506～508
162. Peters J. F., Skowron A, Suraj Z. An application of rough set methods in control design. Fundamenta Informaticae, 2000, 43(2): 269～290
163. Chen W. C, Chang Ni-Bin, Chen Jeng-Chung. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment, Water Research, 2003, 37(1): 95～107
164．项晓娟，谢克明．基于粗糙集神经网络PID的串级控制系统设计[J]．太原理工大学学报，2002，33(6)：620～612
165．李炜，谢刚，谢克明．一种改进型的粗糙-神经网络PID控制器设计[J]．太原理工大学学报，2004，35(3)：251～253
166．黎明．基于模糊集和粗糙集理论的非线性系统建模与控制方法研究[学位论文]．沈阳：东北大学，2003
167. Huang Jinjie, Li Shiyong, Man Chuntao. AT-S Type of Rough Fuzzy Control System and Its Implementation. In: G. Wang et al. (Eds.): RSFDGrC 2003, LNAI 2639: 325～328
168. Zbigniew Suraj, James E Peters, Piotr Grochowalski. A Controller Design for the Khepera Robot: A Rough Set Approach. Fundamenta informaticae. 2005, 67: 219～231
169. Chen W. C, Chang Ni-Bin, Chen Jeng-Chung. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment. Water Research, 2003, 37: 95～107
170. Wang Geng, Zhao Jun, Qian Jixin. A Successive Design Method of Rough Controller using Extra Excitationn. In: L. Wang and Y. Jin (Eds.): FSKD 2005, LNAI 3613: 1266～1270
171. Shen Qiang, Chouchoulas Alexios. A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems[J].Engineering Applications of Artificial Intelligence, 2000, 13(3):263～278
172．冯志鹏等．粗糙集与神经网络集成在故障诊断中应用研究[J]．大连理工大学学报，2003，43(1)：70～76
173．凌维业，贾民平，许飞云．粗糙集神经网络故障诊断系统的优化方法研究[J]．中国电机工程学报，2003，23(5)：98～102
174．郝丽娜，王伟，吴光宇．粗糙集-神经网络故障诊断方法研究[J]．东北大学学报(自然科学版)，2003，23(3)：252～255
175．李千目，戚涌，张宏．基于粗糙集神经网络的网络故障诊断新方法[J]．计算机研究 与发展，2004，41(10)：1696～1702
176．曹云峰，王耀才，王军威．基于粗糙集的容错神经网络故障诊断系统[J]．计算机工程与设计，2006，27(4)：637～639
177．苏宏升，李群湛．基于粗糙集理论和神经网络模型的变电站故障诊断方法[J]．电网技术，2005，29(16)：66～70
178．郝丽娜，徐心和．粗糙集神经网络系统在故障诊断中的应用[J]．控制理论与应用，2001，18(5)：681～685
179．於东军，王士同．基于粗糙集的模糊神经网络建模研究[J]．华东船舶工业学院学报，2000，14(3)：1～6
180．张燕秦，徐向东．用于热力系统建模的基于粗糙集的模糊神经网络[J]．清华大学学报(自然科学版)，2004，44(8)：1083～1086
181．何明，李博，马兆丰．粗糙集理论框架下的神经网络建模研究及应用[J]．控制与决策，2005，20(7)：782～785
182．孙全玲．基于粗集和神经网络的建模方法研究[J]．安徽建筑工业学院学报，2005，13(2)：81～84
183. Nyuyen H S, Skowron A. Boolean reasoning for feature extraction problems[C]. 10th International Symposium on Foundations of Intelligent Systems, Charlotte, North Carolina, USA, 1997:116～127
184. N gnyen S. H, Ngnycn H.S. Some efficient algorithms for rough set methods. In: Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, 1996,1451～1456
185. Snsmaga R..Analyzing discretizations of continuous attributes given a monotonic discrimination function. Intelligent Data Analysis, 1997,1(4): 157～179
186. Dai Jian-Hna, Li Ynan-Xiang. Study on discretization based on rough set theory. In: Proceedings of the first International Conference on Machine Learning and Cybernetics, Beijing, 2002,1371～1373
187. Shi W F, Yang J M, Tang T H. RBF NN Based Marine Diesel Engine Generator Modeling. American Control Conference, 2005:2745～2749
188．刘志远，吕虹剑，陈来九．基于RBF神经网络的单元机组负荷系统建模研究[J]．．控制与决策，18(5)：637～640
189．李艳君，吴铁军，赵明旺．一种新的RBF神经网络非线性动态系统建模方法[J]。系统工程理论与实践，2001，No．3：64～69
190．艾芊，周玉光，顾丹珍等．适用于励磁系统建模的模糊神经网络方法[J]．上海交通大学学报，2005，39(s1)：5～8
191．韩崇昭，党映农．非线性控制系统综合的频域逆系统方法研究[J]．控制与决策，2000，15(5)：545～548
192．扈宏杰，尔联洁，吴云洁．基于神经网络的直流电动机实时跟踪控制[J]．系统工程与电子技术，2001，23(9)：28～30
193．程仁洪，涂莑生．动态解耦条件下异步电机的近似逆系统及应用[J]．南开大学学报(自然科学版)，2002，35(1)：53～57
194．陈文林，郝丽娜，徐心和．粗糙集-神经网络-专家系统混合系统及其应用[J]．计算机工程，2003，29(9)：147～148
195．许志兴，丁运亮，陆金桂．一种基于粗糙集理论的粗糙神经网络构造方法[J]．．南京航空航天大学学报，2001，33(4)：355～359
196．姜惠兰，魏强，唐晓骏．基于模糊神经网络的发电机励磁控制器的研究[J]．电网技术，2005，29(1)：50～55
197．王莉，曹小庆等．电励磁双凸极无刷直流发电机非线性PI调压技术的研究[J]．中国电机工程学报，2006，26(5)：153～158
198．王剑，毛宗源，许敬涛．发电机励磁及调速综合控制可行性分析[J]．中国电机工程学报，2006，26(13)：74～78
199．殷勇，邱明．一种基于高斯核的RBF神经网络学习算法[J]．计算机工程与应用，2002(21)：118～119
200. Skowron A. Rough sets and Boolean reasoning. In: Pedrycz Wed. Granular Computing: An Emerging Paradigm. New York: Physica-Verlag, 2001:95～124
201. Kenne G., Ahmed-Ali T. et al. Nonlinear systems parameters estimation using radial basis function network. Control Engineering Practice, 2006, 14:819～832
202. Xuri Yin, Zhihua Zhou, Ning Li, Shifu Chen. An Approach For Data Filtering Based On Rough set Theory. In: Wang X S, Yu G, Lu H Eds. Lecture Notes in Computer Science 2118, Berlin: Springer-Verlag, 2001,pp.367～374
203. Li Yu-Rong, Jiang Jing-Ping. The Integrated Methodology of Rough Set Theory, fuzzy logic and genetic algorithma for multisensor fusion. Proceedings of the American Control Conference, Arlingtor, 2001:4416～4421
204. Pawlak, Z. A Treatise on Rough Sets. In: Peters, J.F. et al Eds.: Transactions on Rough Sets Ⅳ. Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg New York 2005, LNCS 3700:1～17
205. Peters J. F, Feng H, Ramanna S: Adaptive Granular Control of an HVDC System: A Rough Set Approach. In: G. Wang et al. (Eds.): RSFDGrC 2003, LNAI 2639: 213～220
206. Wei Jin-Mao. Rough Set Based Approach to Selection of Node. International Journal of Computational Cognition, 2003,1(2): 25-40
207. Lin Whei-min, YANG Chin-der, Lin Jia-hong, et al. A Fault Classification Method by RBF Neural Network with OLS Learning Procedure [J]. IEEE Trans, on Power Delivery, 2001,16(4): 473-477
208. Polkowski, L, Skowron, A., Introducing rough mereological controllers: Rough quality control, The Third International Workshop on Rough Sets and Soft Computing, San Jose State University, CA, 1994: 78-85
209. Mrozek A., Plonka L. Knowledge Representation in Fuzzy and Rough Controllers. Fundamenta Informaticae, 1994, 30: 299-311
210. Munakata T. Rough control, Basic ideas applications. In: the Proceeding of the Second. Annual Joint Conference on Information Sciences, Wrightsville Beach, NC, 1995: 340-343
211. Munakata T. Rough Control, A Perspective. In: T. Y. Lin and N. Cercone, Eds. Rough Sets and Data Mining. Analysis for Imprecise Data. Boston, London, Dordrecht: Kluwer Academic Publishers, 1997: 77-88
212. Skowron A., Polkowski L. Rough mereological foundations for design, analysis, synthesis, and control in distributive systems. Information Sciences, 1998, 104: 129-156
213. Radzikowska Anna Maria, Kerre Etienne E. A comparative study of fuzzy rough sets, Fuzzy Sets and Systems, 2002,126(2): 137-155
214. Pawlak Z. AI and intelligent industrial applications: the rough set perspective [J]. Cybernetics and Systems: An International Journal, 2000, 4 (31): 227-252
215. Liang Ji-Ye, Shi ZhongZhi, Li De Yu. Applications of Inclusion Degree in Rough Set Theory. International Journal of Computational Cognition, 2003,1(2): 67-78
216. Hu, X.H. Knowledge Discovery in Databases: An Attribute-oriented Rough Set Approach [D]. Ph.D. Thesis, University of Regina, Canada, 1995
217. Pawlak Z. Rough Sets, Rough Relations and Rough Functions[J]. Fundamenta informaticae, 1996,27(2): 271-276
218. Shu Hongchun,Sun Xianglei,Si Dajun.Study of Fuzzy Controller for Voltage and Reactive Power in Substation Using Rough Set Theory. In: International Conference on Power System Technology, IEEE, 2002, Vol 4: 2452-2456
219. Pawlak Z. Some remarks on conflict analysis. European Journal of Operational Research, 2005,166: 649-654
220. Guo, J., Peter B.: Selecting Input Factors for Clusters of Gaussian Radial Basis Function Networks to Improve Market Clearing Price Prediction. IEEE Transactions on Power Systems, 2003,18(2): 665-671
221. Bazan, J. G, Szczuka, M.: The Rough Set Exploration System. In: Peters, J.F. et al. (Eds.): Transactions on Rough Sets III, Lecture Notes in Computer Science, Vol.3400, Springer-Verlag, Berlin Heidelberg New York, 2005: 37-56
222. Karrari, M, Malik, O. P.: Identification of synchronous generators using adaptive mavelet networks. Electrical Power and Energy Systems, 2005 (27): 113-120
223. Sarimveis H, Doganis P, Alexandridis A. A classification technique based on radial basis function neural networks. Advances in Engineering Software, 2006(37): 218-221
2. Pawlak, Z. Rough Sets and Intelligent Data Analysis. Information Sciences, 2002, 147: 1～12
3. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Puhlishers. 1991
4. Pawlak, Z. Some Issues on Rough Sets. In: Peters, J.F. et al Eds.: Transactions on Rough Sets I. Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg New York 2004, LNCS 3100:1～58
5．王国胤．Rough集理论与知识获取[M]．西安：西安交通大学出版社，2001
6．张文修 等 编著．粗糙集理论与方法[M]．北京：科学出版社，2001
7．吴明芬．粗糙集理论的研究现状与前景[J]．五邑大学学报(自然科学版)，2002，16(2)：16～21
8. Munakata, T., Pawlak, Z. Rough Control: Application of Rough Set Theory to Control. Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, September 2～5,1996, volume 1:209～218
9．张文修，吴伟志．粗糙集理论介绍和研究综述[J]．模糊系统与数学，2000，14(4)：1～12
10. Lambert-Tomes G, et al. Data Mining into a Control Center Database via Rough Set Techniques. In: Proc. of the International Conference on Intelligent System Application to Power Systems (ISAP' 97). Seoul (Korea), 1997, 246～250
11．束洪春，孙向飞，于继来．粗糙集理论在电力系统中的应用[J]．电力系统自动化，2004，28(3)：90～95
12．张琦，韩祯祥，文福拴．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[J]．中国电力，1998，31(4)：32～35
13．束洪春，孙向飞，司大军．基于粗糙集理论的配电网故障诊断研究[J]．中国电机工程学报，2001，21(10)：73～78
14．张琦，韩祯祥等．用于暂态稳定评估的人工神经网络输人空间压缩方法[J]．电力系统自动化，2001，25(2)：32～39
15. Liu ShuLin, Shi Wengang. Rough Set Based Intelligence Diagnostic System for Valves in Reciprocating Pumps. In: IEEE International Conference on Systems, Man, and Cybernetics, 2001, volume 1:353～358
16. KHOO L. P., Tor S. B. Li J. R. A Rough Set Approach to the Ordering of Basic Events in a Fault Tree for Fault Diagnosis. Advanced manufacturing technology, 2001, 17(10): 769～774
17. Ou Jian, Sun Caixin, Bi Weimin, et al. A steam turbine-generator vibration fault diagnosis method based on rough set. In: Proceedings of IEEE International Conference on Power System Technology, 2002, volume 3:1532～1534
18. Shu Hongchun, Sun Xiangfei, Si Dajun. A new method for locating faults on transmission lines based on rough set and FNN. In: Proceedings of IEEE International Conference on Power System Technology, 2002, volume 4:2584～2588
19. Cao L, Cao C, Guo Z, et al. The research of fault diagnosis for fuel injection system of diesel engine with ANN based on rough sets theory. In: Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, 2002:410～414
20. Francis E.H. Tay, Shen Lixiang. Fault diagnosis based on Rough Set Theory. Engineering Applications of Artificial Intelligence, 2003, 16:39～43
21. Wang QingHui, Li Jing Rong. A rough set-based fault ranking prototype system for fault diagnosis. Engineering Applications of Artificial Intelligence, 2004, 17:909～917
22. Li Ru-qiang, Chen Jin, et al. Fault diagnosis of rotating machinery based on SVD, FCM and RST. Int J Adv Manuf Technol, 2005
23. Caccavale F., Villani L. Fault Diagnosis for Industrial Robots. Fault Diagnosis and Fault Tolerance for Mechatronic Systems, STAR 1, 2003:85～108
24. Tsumoto S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Information Sciences, 2004, 162:65～80
25. Zhang Q, Han Z, et al. A new approach for fault diagnosis in power systems based on rough set theory. Proceedings of the 4th International Conference on Advances in Power System Control, Operation and Management, 1997, 2:597～602
26. Wakulicz-Deja A, Paszek P. Diagnose progressive encephalopathy applying the rough set theory. International journal of medical informatics, 1997, 46:119～127
27．张腾飞,王锡淮，等．粗糙集理论在故障诊断中的应用综述[J]．上海海事大学学报，2005，26(4)：20～25
28．张琦，韩祯祥，曹绍杰，等．用于暂态稳定评估的人工神经网络输人空间压缩方法[J]．电力系统自动化，2001，25(2)：32～39
29. Gu X P, Tso S K. Application Rough Set Concept to Neural-network-based Transient Stability Classification of Power Systems. In: Proceeding of the 5th International Conference on Advance in Power System Control, Operation and Management, Hong Kong:IEE.2000:400～404
30．顾雪平，张文朝．基于Tabu搜索技术的暂态稳定分类神经网络的输人特征选择[J]。 中国电机工程学报，2002，22(7)：66～70
31．张庆宝，程浩忠，等．基于粗糙集属性约简算法和支持向量机的短期负荷预测[J]．电网技术，2006，30(8)：56～59
32．冯丽，邱家驹．粗糙集理论在短期电力负荷预测中的应用[J]．华北电力技术，2004(4)：5～7
33．钟波，周家启，肖智．基于粗糙集与神经网络的电力负荷新型预测模型[J]．系统工程理论与实践，2004，24(6)：113～119
34．谢宏，程浩忠，等．基于粗糙集理论建立短期电力负荷神经网络预测模型[J]．中国电机工程学报，2003，23(11)：1～4
35. Xiao J. M., Zhang T.F., Wang X.H. Ship power load prediction based on RST and RBF neural networks. In: J. Wang, X. Liao, and Z. Yi (Eds.): Advances in Neural Networks. Lecture Notes in Computer Science, LNCS 3498. Springer-Verlag, Berlin Heidelberg New York, 2005:648～653
36．赵卫东，陈国华．粗集与神经网络的集成技术研究[J]．系统工程与电子技术，2002，24(10)：103～107
37. Ohrn A. Rough Logic Control: a new approach to automatic control?[J].[Project Report] Norwegian University of Science and Technology, Trondheim, Norway, 1993
38. Mrozek, A., Plonka, L., Kedziera J. The Methodology of Rough Controller Synthesis[J]. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996, 2(9): 1135～1139
39．杨会志，于春田．一种浮选过程控制的新策略——粗糙控制[J]．矿业研究与开发，1999，19(5)：20～23
40．谢克明，杨静．粗糙集理论及其在智能控制领域的应用前景[J]．太原理工大学学报，1999，30(4)：338～342
41．杨国豪 编著．船舶电站控制装置及动态模拟系统[M]．大连：大连海事大学出版社，1999
42．黄伦坤，朱正鹏，刘宗德编．船舶电站及其自动装置[M]．人民交通出版社，1981
43．陆让之，张声钺，黄春兰编．船舶电机学[M]．人民交通出版社，1986
44．陈宗海 编著．过程系统建模与仿真[M]．合肥：中国科学技术大学出版社，1997
45．黄曼磊，李殿璞，唐嘉亨．船舶电站柴油机调速系统H_∞控制器的设计[J]。中国造船，2001(4)：46～51
46．袁季修．电力系统安全稳定控制[M]．北京：中国电力出版社，1996
47．[苏]．柯瓦连科著．钱瑞麟译．船舶同步发电机自动励磁调节和稳定性[M]．国防工业出版社，1980
48．卢强，王仲鸿，韩英铎．输电系统最优控制[M]．北京：科学出版社，1982
49．卢强，孙元章．电力系统非线性控制[M]．北京：科学出版社，1993
50. Sun C, Zhao Z. Design of Nonlinear Robust Excitation Control for Multi-Machine Power System. IEE Proc. Gener. Transm. Distrib. 1996, 143(3): 253～257
51. Samarasing V. G. D. C, Pahalawaththa N.C. Design of a Robust Variable Structure Controller for Improving Power System Dynamic Stability. International Journal of Electrical Power & Energy Systems, 1996, 18(8): 519～525
52．刘清．Rough集及Rough推理[M]．北京：科学出版社，2001
53. Skowron A, et al. The discernibility matrices and functions in information system[A]. In: Slowinski R. et al, Intelligent Decision Support-Handbook of Applications and Advances of the rough set theory[C]. Kluwer Academic Publishers. 1992:331～363
54．王东锴，梁樑．含冲突的决策表中的决策规则发现[J]．系统工程，2003，21(1)：8～11
55．王国胤，于洪，杨大春．基于条件信息熵的决策表约简[J]．计算机学报，2002，25(7)：759～766
56．王国胤．Rough集理论代数与信息论观点的关系研究[J]．世界科技研究与发展，2002，24(5)：20～26
57. Kryszkiewics M. Rough set approach to incomplete information system[J]. Information science, 1998, 112:39～49
58．林泰崴，陈昭炯，叶东毅．不完备信息系统下限制容差关系模型的改进[J]．福州大学学报(自然科学版)，2004，vol．32 supp．：39～42
59. Stefanowski J, Tsoukias A. On the Extension of Rough Sets under Incomplete Information. In: Zhong N, et al, Eds. Proc of the 7th Int. Workshop on New Directions in Rough, Data Mining, and Granular-soft Computer. Berlin:Springer-Verleg, 1999, 73～81
60．王国胤．Rough集理论在不完备信息系统中的扩充[J]．计算机研究与发展，2002，39(10)：1238～1243
61．丛爽著．神经网络、模糊系统及其在运动控制中的应用[M]．合肥：中国科学技术大学出版社，2001．5
62．徐丽娜 编著．神经网络控制[M]．北京：电子工业出版社，2003．2
63．魏海坤 编著．神经网络结构设计的理论与方法[M]．北京：国防工业出版社，2005
64. Jianmei Xiao. Application of RBF Neural Network to Freeway Traffic Flow Modeling. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, P.R. China, 2004:5208～5211
65. Chen S. Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks. IEEE Transactions on Neural Networks, 1999, 10(5): 1239～1243
66．赵清林，郭艳兵，梅强等．确定RBF神经网络中心点的方法综述[J]．广东自动化与信息工程，2002(2)：13～15
67. http://www.dmresearch.net/html/content/paper-on-data-mining/1000002482.php
68．高隽 编著．智能信息处理方法导论[M]．机械工业出版社，2004．6
69．候利娟 等．粗糙集理论中的离散化问题[J]．计算机科学，2000，27(12)：89～94
70. Nguyen H S, Skowron A. Quantization of real value attributes. Proceeding of Second Joint Annual Conference on Information Science, Wrightwille Beach, North Carolina, 1995:34～37
71. Xiang Xin-jian, Stolle.M. An algorithm of discretization of continuous attributes in rough sets based on cluster. Journal of Zhejiang University of Science and Technology, 2003, 15(3): 154～157
72. Amitava Roy, Sankar K.Pal. Fuzzy discretization of feature space for a rough set classifer. Pattern Recognition Letters, 2003, 24:895～902
73．张文宇，贾嵘，薛惠锋等．知识发现过程中连续属性离散化方法研究[J]．西安理工大学学报，2002，18(3)：249～253
74．苗夺谦．Rough Set理论中连续属性的离散化方法[J]．自动化学报．2001，27(3)：296～302
75. Eberhart R. C, Kennedy J. Particles Swarm Optimization. IEEE International Conference on Neural Network, Perth, Australia, 1995:1942～1948
76. Kennedy J, Eberhart R. C. Swarm Intelligence.San Francisco, CA:Morgan Kaufmann, 2001
77. Zwe-Lee Gaing. Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints, IEEE Transactions on Power Systems, August 2003, 18(3): 1187～1195
78. Pawlak Z. Rough sets theory and its applications to data nanlysis[J]. Cybernetics and Systems, 1998, 29: 661～668
79．刘少辉等．Rough集高效算法的研究[J]．计算机学报，2003，26(5)：524～529
80. Hu X H, Cercone N. Learning in relational databases: A rough set approach. Computational Intelligence, 1995, 11(2): 323～337
81．叶东毅，陈昭炯．一个新的差别矩阵及其求核方法[J]．电子学报，2002，30(7)：1086～1088
82．王国胤．决策表核属性的计算方法[J]．计算机学报．2003，5，26(5)：611～615
83．徐一新，叶东毅．知识约简的差别矩阵启发式算法[J]．福州大学学报(自然科学版)，2000，28(3)：120～123
84．刘文军等．基于可辨识矩阵和逻辑运算的属性约简算法的改进[J]．模式识别与人工智能，2004，17(1)：119-123
85. Mi J. et al. Approaches to knowledge reduction based on variable precision rough set model[J]. Information Sciences, 2004, 159(3): 255～272
86. Zhang M. et al. A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory[J]. Expert Systems, 2003, 20(5): 298～304
87. Wong S K M, Ziarko W. On Optimal Decision Rules in Decision Ttables. Bulletin of Polish Academy of Science: 1985,33(12): 693～696
88. http://www.ics.uci.edu/～mlearn/MLRePOSitory.html
89．刘文军等．一种求粗糙集中最小属性约简的新算法[J]．北京师范大学学报(自然科学版)，2004，40(1)：8～12
90. Jelonek J, Krawiec K, et al. Rough set reduction of attributes and their domains for neural networks. International Journal of Computational Intelligence, 1995, 11(2): 339～347
91．苗夺谦，胡桂荣．知识约简的一种启发式算法[J]．计算研究与发展，1999，36(6)：681～684
92．张腾飞，肖健梅，王锡淮．粗糙集理论中属性相对约简算法[J]．电子学报，2005，33(11)：2080～2083
93．何伟等．不完备信息系统下的属性约简算法[J]．计算机科学，2004，31(2)：117～119
94. Liang, Jiye, Xu, Zongben. The algorithm on knowledge reduction in incomplete information systems[J]. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2002, 10(1): 95～103
95. Qiao, Bin, Zhang, Guohong, Jiang, Jingping. Hierarchical reduction approach of rough sets to incomplete systems[A]. IEEE Region 10th Annual International Conference, Proceedings/TENCON[C], 2002, vol.1: 1～4
96. Sun, Hui-Qin, Xiong, Zhang. Finding minimal reducts from incomplete information systems[A]. International Conference on Machine Learning and Cybernetics[C], 2003, vol.1: 350～354
97．王珏，苗夺谦，周育健．关于Rough Set理论与应用的综述[J]．模式识别与人工智能，1996，9(4)：337～344
98. Yang Yanyi, Chiam T. C. Rule discovery based on rough set theory. 12th International Symposium on Integrated Ferroelectrics, Aachen, Germany, 2000:11～16
99．刘文军等．基于区分矩阵求决策算法的约简[J]．北京师范大学学报(自然科学版)，2003，39(3)：311～315
100. Fleix R, Ushio T. Rough Sets-based Machine Learning Using a Binary Discernibinity Matrix[J]. IPMM, 1999:299～305
101．支云天，苗夺谦．二进制可辨矩阵的变换及高效属性约简算法的构造[J]．计算机科学，2002，29(2)：140～142
102．叶东毅．一个新的二进制可辨识矩阵及其核的计算[J]．小型微型计算机系统，2004，25(6)：965～967
103. Xihuai, Wang, Tengfei, Zhang, etc. A New Algorithm for Relative Attribute Reduction in Decision Table, The 6th World Congress on Intelligent Control and Automation, Dalian, P.R. China, 2006:4051～4054
104．施伟锋，郑华耀．船舶自动化电站系统仿真[J]．系统仿真学报，2003，15(9)：1249～1252
105．黄曼磊，李殿璞．船舶电站同步发电机调压系统的数学模型[J]．哈尔滨工程大学学报，2004，25(3)：305～308
106．Fitzgerald AE，Kingsley，C等著．刘新正，苏少平，高琳 等译．电机学[J]．北京：电子工业出版社，2004
107. ONG, Chee-Mun. Dynamic Simulation of Electric Machinery: Using MATLAB/Simulink. Prentice Hall, 1998
108．吴天明 等编著．MATLAB电力系统设计与分析[M]．北京：国防工业出版社，2004
109．黎明，张化光．基于粗糙集的神经网络建模方法研究[J]．自动化学报，2002，28(1)：27～33
110．刘璨，陈统坚，彭永红等．基于粗集理论的模糊神经网络建模方法研究[J]．中国机械工程，2001，12(11)：1256～1259
111. Qin Zhongguang, Mao Zongyuan. A New Algorithm for Neural Network Architecture Study. Proceedings of 3th World Congress on Intelligent Control and Automation, Heifei, P. R. China, 2000: 795～799
112．陈遵德．Rough Set神经网络智能系统及其应用[J]．模式识别与人工智能，1999，12(1)：1～5
113. Li Qiu-dan, Chi Zhong-xian, Shi Wen-bing. Application of Rough Set Theory and Artificial Neural Network for Load Forecasting. Proceedings of the first International Conference on Machine Leaning and Cybernetics, Beijing, 2002: 1148～1152
114. 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 Application, 2000, 18:65～74
115. Son Nguyen H, Szczuka M, Slezak D. Neural Network Design: Rough Set Approach to Real-valued Data. Komorowski J, Ztkow J Eds, The First European Symposium on Principles of Data Mining and Knowledge Discovery. Berlin: Trondheim, Norway, Springer-Verlag, 1997:359～366
116. Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design[M]. PWS Publishing Company, 1996
117．徐晓霞，陈涛，王晓升．基于正交最小二乘算法的小波神经网络[J]．电子学报，1998，26(10)：115～117
118. Micchelli C. Interpolation of scattered data: distance matrices and conditionally positive definite functions. Constructive Approximation, 1986(2): 11～22
119. Wang, Xi, Zhang, T., Xiao, J.: Ship Synchronous Generator Modeling Based on RST and RBF Neural Networks. In: Wang, J. et al. (Eds.): Advances in Neural Networks. Lecture Notes in Compute Science, Vol. 3972, Springer-Verlag, Berlin Heidelberg New York, 2006:1363～1369
120．施亿生．船舶电站[M]．北京：国防工业出版社，1983
121．施伟锋，陈子顺，汤天浩．船舶发电机励磁系统的CMAC神经网络并行控制[J]．电网技术，2005，29(4)：31～35．
122．黄曼磊．船舶电站同步发电机H_∞调压器的仿真研究[J]．哈尔滨工程大学学报，2004，25(2)：162～166
123. Lu Qiang, Sun Yuan-zhang, Mei Shengwei. Nonlinear control systems and power system dynamics[M]. Boston: Kluwer Academic Publishers, 2001
124．唐巍，郭镇明，李殿璞．混沌优化用于电力系统静态负荷模型参数辨识[J]．电力系统自动化，2000，24(13)：27～30
125．刘金琨 著．先进PID控制及其MATLAB仿真[M]．北京：电子工业出版社，2003
126．厉启宏，宋华屏．船舶交流发电机调压器差动横向电流补偿工作原理分析[J]．上海造船，2004，1：61～65
127. Dai J. J., Xiao Di, et al. Emergency Generator Startup Study of a Hydro Turbine Unit for a Nuclear Generation Facility[J]. IEEE Trans. on Industry Applications, 2004, 40(5): 1191～1999
128. Mark J. L. Orr. Matlab Functions for Radial Basis Function Networks, Technical report, Institute for Adaptive and Neural Computation, Division of Informatics, Edinburgh University, 1999
129．戴先中．多变量非线性系统的神经网络逆控制方法[M]．北京：科学出版社，2005
130．张腾．神经网络逆系统方法及其在电力系统控制中的应用[学位论文]．南京：东南大学，2002
131．陈双叶，易继锴．基于粗糙集理论的模糊模型及其在复杂控制系统中的应用[J]。 控制与决策，2002，vol．17，suppl：747～751
132．谭天乐．基于粗糙集的过程建模、控制与故障诊断[学位论文]．杭州：浙江大学，2003
133．郝丽娜，徐心和．基于粗糙集理论的规则修正方法[J]．控制与决策，2002，17(5)：614～616
134．唐肝翌，邓辉文，张栒．规则不完备的模糊系统新规则在线生成[J]．计算机工程与应用，2006，63～65
135．叶东毅．基于粗糙隶属函数的规则组合可信度计算与推理[J]．福州大学学报(自然科学版)，2002，30(3)：294～297
136．王秀，叶东毅．基于分布约简的获取规则的增量式方法[J]．福州大学学报(自然科学版)，2005，33(1)：16～19
137．王加成，王庆东等．粗糙集理论中规则和综合决策的可信度计算的新方法[J]．信息与控制，2003，32(7)：724～728
138. Tan Tian-Le, Song Zhi-Huan, Li Ping. Rough set based modeling and controller design in an internal model control system[J]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2003, volume 4:3559～3564
139．徐建英，刘贺平．永磁同步电动机参考模型逆线性二次型最优电流控制调速系统[J]．中国电机工程学报，2007，27(15)：21～27
140．宋夫华，李平．基于支持向量机a阶逆系统方法的非线性内模控制[J]．自动化学报，2007，33(7)：778～781
141．程仁洪．基于近似逆系统的异步电机复合控制方法[J]．电机与控制学报，2001，5(3)：163～165
142．史伟伟，唐国庆，李俊．动态电压恢复器的双前馈控制策略研究[J]，电力自动化设备，2007，27(2)：11～15
143．王永林，王杰．神经网络前馈逆控制在交流伺服系统中的应用[J]．郑州大学学报(自然科学版)，2000，32(3)：51～56
144．曲东才，何友．一种BPMFN逆模型补偿的复合控制结构方案研究[J]．宇航学报，2006，27(6)：1414～1418
145．董立新，肖登明等．基于油中溶解气体分析数据挖掘的变压器绝缘故障诊断[J]．电力系统自动化，2004，28(15)：85～89
146．白如江．基于粗糙集和RBF神经网络的文本自动分类方法[J]．现代图书情报技术，2006，vol．6：47～51
147．孟科，张恒喜等．基于粗糙集的RBF神经网络在装备研制费用预测中的应用[J]．装备指挥技术学院学报，2006，17(5)：14～17
148. Wu Di, Zhang Yaping, Wang Xin. RBF neural network for hierarchical intrusion detection systems based on RS feature selection[J]. Journal of Computational Information Systems, 2007, 3(4): 1335～1342
149. Zhang Xu, Sun Jianbo, Guo Chen. A novel method of intelligent fault diagnosis for diesel engine[A]. Proc. of the 6th Word Congress on Intelligent Control and Automation[C], vol.2: 5739～5743
150. Liu Hongjie, Feng Boqin, Xia Kewen. Seismic reservoir oil-gas prediction study based on rough set and RBF network[A]. Proc. of the 6th World Congress on Intelligent Control and Automation[C], vol. 1:4229～4233
151．李昌彪，夏克文等．一种基于属性重要性的粗糙RBF神经网络[J]．控制与决策，2006，21(7)：821～824
152．何明，冯博琴等．一种基于粗糙集的粗糙神经网络构造方法[J]．西安交通大学学报，2004，38(12)：1240～1242
153. Sienkiewicz J Rough set and rough function approaches to the control algorithm reconstruction. Institute of Computer Science Reports, Warsaw University of Technology, Warsaw, 1996
154. Mrozek A. Rough sets in computer implementation of rule-based control of industrial process. In: R. Slowinski (Ed.), Intelligent Decision Support: Handbook of applications and advances of the Rough sets theory. Kluwer academic publishers, Boston, 1992:19～31
155. Pawlak Z. Rough real functions and rough controllers. In: Lin T.Y, Cercone N. (Eds.), Rough Sets and Data. Mining. Analysis of Imprecise Data, Kluwer Academic Publishers, Boston 1997:139～147
156. Mrozek A. Use of rough sets and decision tables for implementing rule-based control of industrial Process. Bulletin of the Polish Academy of Sciences: Technical Sciences, 1986, 34(5-6): 357～371
157. Czogala E, Mrozek A, Pawlak Z. The idea of a rough fuzzy controller and its application to the stabilization of a pendulum-car system[J]. Fuzzy Sets and Systems, 1995, 72:61～73
158. Plonka L and Mrozek A. Rule-based stabilization of the inverted pendulum[J]. Computational Intelligence, 1995, 11(2): 348～356
159. Peters J. F., Ziarko K, Ramanna S. Approximate Time Rough Control: Concepts and Application to Satellite Attitude Control, Proc. of RSCTC'98, Warsaw, Poland, 1998: 491～498
160. Peters J. F., Ziaei K, Generating rules in selecting controller gains: a combined rough set/fuzzy sets approach, IEEE Canadian Conference on Electrical and Computer Engineering, 1998, 1:233～236
161. Tseng C H, Lin T Y, Chi C W. Adaptive aggregation of modular control. Proc. IEEE fuzzy sets and systems, 1996: 506～508
162. Peters J. F., Skowron A, Suraj Z. An application of rough set methods in control design. Fundamenta Informaticae, 2000, 43(2): 269～290
163. Chen W. C, Chang Ni-Bin, Chen Jeng-Chung. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment, Water Research, 2003, 37(1): 95～107
164．项晓娟，谢克明．基于粗糙集神经网络PID的串级控制系统设计[J]．太原理工大学学报，2002，33(6)：620～612
165．李炜，谢刚，谢克明．一种改进型的粗糙-神经网络PID控制器设计[J]．太原理工大学学报，2004，35(3)：251～253
166．黎明．基于模糊集和粗糙集理论的非线性系统建模与控制方法研究[学位论文]．沈阳：东北大学，2003
167. Huang Jinjie, Li Shiyong, Man Chuntao. AT-S Type of Rough Fuzzy Control System and Its Implementation. In: G. Wang et al. (Eds.): RSFDGrC 2003, LNAI 2639: 325～328
168. Zbigniew Suraj, James E Peters, Piotr Grochowalski. A Controller Design for the Khepera Robot: A Rough Set Approach. Fundamenta informaticae. 2005, 67: 219～231
169. Chen W. C, Chang Ni-Bin, Chen Jeng-Chung. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment. Water Research, 2003, 37: 95～107
170. Wang Geng, Zhao Jun, Qian Jixin. A Successive Design Method of Rough Controller using Extra Excitationn. In: L. Wang and Y. Jin (Eds.): FSKD 2005, LNAI 3613: 1266～1270
171. Shen Qiang, Chouchoulas Alexios. A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems[J].Engineering Applications of Artificial Intelligence, 2000, 13(3):263～278
172．冯志鹏等．粗糙集与神经网络集成在故障诊断中应用研究[J]．大连理工大学学报，2003，43(1)：70～76
173．凌维业，贾民平，许飞云．粗糙集神经网络故障诊断系统的优化方法研究[J]．中国电机工程学报，2003，23(5)：98～102
174．郝丽娜，王伟，吴光宇．粗糙集-神经网络故障诊断方法研究[J]．东北大学学报(自然科学版)，2003，23(3)：252～255
175．李千目，戚涌，张宏．基于粗糙集神经网络的网络故障诊断新方法[J]．计算机研究 与发展，2004，41(10)：1696～1702
176．曹云峰，王耀才，王军威．基于粗糙集的容错神经网络故障诊断系统[J]．计算机工程与设计，2006，27(4)：637～639
177．苏宏升，李群湛．基于粗糙集理论和神经网络模型的变电站故障诊断方法[J]．电网技术，2005，29(16)：66～70
178．郝丽娜，徐心和．粗糙集神经网络系统在故障诊断中的应用[J]．控制理论与应用，2001，18(5)：681～685
179．於东军，王士同．基于粗糙集的模糊神经网络建模研究[J]．华东船舶工业学院学报，2000，14(3)：1～6
180．张燕秦，徐向东．用于热力系统建模的基于粗糙集的模糊神经网络[J]．清华大学学报(自然科学版)，2004，44(8)：1083～1086
181．何明，李博，马兆丰．粗糙集理论框架下的神经网络建模研究及应用[J]．控制与决策，2005，20(7)：782～785
182．孙全玲．基于粗集和神经网络的建模方法研究[J]．安徽建筑工业学院学报，2005，13(2)：81～84
183. Nyuyen H S, Skowron A. Boolean reasoning for feature extraction problems[C]. 10th International Symposium on Foundations of Intelligent Systems, Charlotte, North Carolina, USA, 1997:116～127
184. N gnyen S. H, Ngnycn H.S. Some efficient algorithms for rough set methods. In: Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, 1996,1451～1456
185. Snsmaga R..Analyzing discretizations of continuous attributes given a monotonic discrimination function. Intelligent Data Analysis, 1997,1(4): 157～179
186. Dai Jian-Hna, Li Ynan-Xiang. Study on discretization based on rough set theory. In: Proceedings of the first International Conference on Machine Learning and Cybernetics, Beijing, 2002,1371～1373
187. Shi W F, Yang J M, Tang T H. RBF NN Based Marine Diesel Engine Generator Modeling. American Control Conference, 2005:2745～2749
188．刘志远，吕虹剑，陈来九．基于RBF神经网络的单元机组负荷系统建模研究[J]．．控制与决策，18(5)：637～640
189．李艳君，吴铁军，赵明旺．一种新的RBF神经网络非线性动态系统建模方法[J]。系统工程理论与实践，2001，No．3：64～69
190．艾芊，周玉光，顾丹珍等．适用于励磁系统建模的模糊神经网络方法[J]．上海交通大学学报，2005，39(s1)：5～8
191．韩崇昭，党映农．非线性控制系统综合的频域逆系统方法研究[J]．控制与决策，2000，15(5)：545～548
192．扈宏杰，尔联洁，吴云洁．基于神经网络的直流电动机实时跟踪控制[J]．系统工程与电子技术，2001，23(9)：28～30
193．程仁洪，涂莑生．动态解耦条件下异步电机的近似逆系统及应用[J]．南开大学学报(自然科学版)，2002，35(1)：53～57
194．陈文林，郝丽娜，徐心和．粗糙集-神经网络-专家系统混合系统及其应用[J]．计算机工程，2003，29(9)：147～148
195．许志兴，丁运亮，陆金桂．一种基于粗糙集理论的粗糙神经网络构造方法[J]．．南京航空航天大学学报，2001，33(4)：355～359
196．姜惠兰，魏强，唐晓骏．基于模糊神经网络的发电机励磁控制器的研究[J]．电网技术，2005，29(1)：50～55
197．王莉，曹小庆等．电励磁双凸极无刷直流发电机非线性PI调压技术的研究[J]．中国电机工程学报，2006，26(5)：153～158
198．王剑，毛宗源，许敬涛．发电机励磁及调速综合控制可行性分析[J]．中国电机工程学报，2006，26(13)：74～78
199．殷勇，邱明．一种基于高斯核的RBF神经网络学习算法[J]．计算机工程与应用，2002(21)：118～119
200. Skowron A. Rough sets and Boolean reasoning. In: Pedrycz Wed. Granular Computing: An Emerging Paradigm. New York: Physica-Verlag, 2001:95～124
201. Kenne G., Ahmed-Ali T. et al. Nonlinear systems parameters estimation using radial basis function network. Control Engineering Practice, 2006, 14:819～832
202. Xuri Yin, Zhihua Zhou, Ning Li, Shifu Chen. An Approach For Data Filtering Based On Rough set Theory. In: Wang X S, Yu G, Lu H Eds. Lecture Notes in Computer Science 2118, Berlin: Springer-Verlag, 2001,pp.367～374
203. Li Yu-Rong, Jiang Jing-Ping. The Integrated Methodology of Rough Set Theory, fuzzy logic and genetic algorithma for multisensor fusion. Proceedings of the American Control Conference, Arlingtor, 2001:4416～4421
204. Pawlak, Z. A Treatise on Rough Sets. In: Peters, J.F. et al Eds.: Transactions on Rough Sets Ⅳ. Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg New York 2005, LNCS 3700:1～17
205. Peters J. F, Feng H, Ramanna S: Adaptive Granular Control of an HVDC System: A Rough Set Approach. In: G. Wang et al. (Eds.): RSFDGrC 2003, LNAI 2639: 213～220
206. Wei Jin-Mao. Rough Set Based Approach to Selection of Node. International Journal of Computational Cognition, 2003,1(2): 25-40
207. Lin Whei-min, YANG Chin-der, Lin Jia-hong, et al. A Fault Classification Method by RBF Neural Network with OLS Learning Procedure [J]. IEEE Trans, on Power Delivery, 2001,16(4): 473-477
208. Polkowski, L, Skowron, A., Introducing rough mereological controllers: Rough quality control, The Third International Workshop on Rough Sets and Soft Computing, San Jose State University, CA, 1994: 78-85
209. Mrozek A., Plonka L. Knowledge Representation in Fuzzy and Rough Controllers. Fundamenta Informaticae, 1994, 30: 299-311
210. Munakata T. Rough control, Basic ideas applications. In: the Proceeding of the Second. Annual Joint Conference on Information Sciences, Wrightsville Beach, NC, 1995: 340-343
211. Munakata T. Rough Control, A Perspective. In: T. Y. Lin and N. Cercone, Eds. Rough Sets and Data Mining. Analysis for Imprecise Data. Boston, London, Dordrecht: Kluwer Academic Publishers, 1997: 77-88
212. Skowron A., Polkowski L. Rough mereological foundations for design, analysis, synthesis, and control in distributive systems. Information Sciences, 1998, 104: 129-156
213. Radzikowska Anna Maria, Kerre Etienne E. A comparative study of fuzzy rough sets, Fuzzy Sets and Systems, 2002,126(2): 137-155
214. Pawlak Z. AI and intelligent industrial applications: the rough set perspective [J]. Cybernetics and Systems: An International Journal, 2000, 4 (31): 227-252
215. Liang Ji-Ye, Shi ZhongZhi, Li De Yu. Applications of Inclusion Degree in Rough Set Theory. International Journal of Computational Cognition, 2003,1(2): 67-78
216. Hu, X.H. Knowledge Discovery in Databases: An Attribute-oriented Rough Set Approach [D]. Ph.D. Thesis, University of Regina, Canada, 1995
217. Pawlak Z. Rough Sets, Rough Relations and Rough Functions[J]. Fundamenta informaticae, 1996,27(2): 271-276
218. Shu Hongchun,Sun Xianglei,Si Dajun.Study of Fuzzy Controller for Voltage and Reactive Power in Substation Using Rough Set Theory. In: International Conference on Power System Technology, IEEE, 2002, Vol 4: 2452-2456
219. Pawlak Z. Some remarks on conflict analysis. European Journal of Operational Research, 2005,166: 649-654
220. Guo, J., Peter B.: Selecting Input Factors for Clusters of Gaussian Radial Basis Function Networks to Improve Market Clearing Price Prediction. IEEE Transactions on Power Systems, 2003,18(2): 665-671
221. Bazan, J. G, Szczuka, M.: The Rough Set Exploration System. In: Peters, J.F. et al. (Eds.): Transactions on Rough Sets III, Lecture Notes in Computer Science, Vol.3400, Springer-Verlag, Berlin Heidelberg New York, 2005: 37-56
222. Karrari, M, Malik, O. P.: Identification of synchronous generators using adaptive mavelet networks. Electrical Power and Energy Systems, 2005 (27): 113-120
223. Sarimveis H, Doganis P, Alexandridis A. A classification technique based on radial basis function neural networks. Advances in Engineering Software, 2006(37): 218-221