面向概念设计的定性建模技术研究
|
摘要
概念设计作为产品设计的一个重要阶段,在产品全生命周期中具有举足轻重的作用。定性方法和数字样机作为产品设计与分析的有效辅助工具,扩展了设计思维,为产品创新提供了崭新的工具。传统的定量数字样机是在不同时间点变量的数字值集合的层次上进行系统行为推理,对于不精确和非量值的数据缺乏必要的支持。针对产品概念设计阶段存在的大量定性知识和传统定量数字样机在概念设计阶段应用中存在的不足,本文提出了定性数字样机的概念,并研究了定性数字样机一些关键技术问题。
在对产品概念设计的特点、传统数字样机系统的建立与应用、定性建模与仿真理论分析与总结的基础上,本文从目标、功能、能力、技术组成四个方面对定性数字样机进行定义,并采用面向对象的体系框架构建策略实现了定性数字样机体系框架的构建。
针对概念设计阶段所识别系统组成要素间所具有的有机联系和相互作用关系,以及结构化分析方法在定性知识分析上的优势,本文提出了基于结构模型化的定性数字样机建模方法。通过对系统变量间的相互影响关系进行结构化分析与描述,按照定性仿真模型中的代数约束和定性约束形式在所有具有直接因果影响关系的变量间生成相应的约束形式,并进行约束一致性、行为一致性和量纲一致性过滤,生成可直接用于仿真的定性微分方程。
针对Kuipers定性仿真方法实际具备约束满足类问题特性的特点,将其看着是求解一类约束满足问题,从提高仿真效率、方便仿真建模的角度,将Kuipers定性仿真方法根据约束逻辑程序语言进行改进。根据约束求解是基于“先生成后检验”的原则,在逻辑语言的基础上添加了对约束的求解能力,使之更适合于定性知识的应用和程序实现。
定性仿真由于其充分不完备性,易导致仿真出现大量冗余结果,当模型量比较大时进行配对组合、全局一致性过滤会非常困难,针对这种情况本文提出了基于强连通图和遗传算法的两种模型分解方法,根据对象系统的实际情况,按照不同的方法进行系统模型分解,并对分解之后的子系统按照其在原系统中的位置进行重新组合,构成新的简化系统。分解后的各个子系统单独进行仿真,其输出结果即为与之相连的子系统的输入。模型分解将完整的配对组合和冗余行为过滤分散在各个子系统中分别进行,所以可以极大提高复杂系统的仿真效率、降低仿真难度。
在定性仿真中引入定量知识,能够减少定性仿真中由定性计算导致的模糊性、构造不同层次的系统模型、较为精确的定义系统及其行为,从而减少许多不必要的仿真分支,节省大量无谓的计算,能够很好的提高仿真效率,满足不同的应用需求。本文从扩展系统知识应用的角度,采用模糊数学方法构造定量信息与定性信息相集成的定性仿真算法,实现定性定量知识综合的数字样机。基于模糊数学的定性定量知识结合的仿真方法为有效应用产品概念设计阶段的定量信息提供了有效方法。
Product conceptual design activities play an important role in product life cycle. As effective auxiliary product design and analysis tools, qualitative methods and digital prototyping can extend engineers'design mentality and provide new tools for product innovation. Traditional quantitative digital prototyping is lack of the support of non-number and inaccurate data on reason the system behavior. In fact, there is a large number of qualitative knowledge exist in conceptual design stage. Directed towards those reasons a concept of qualitative digital prototyping is proposed in this dissertation, and some key technical problems are researched.
After the author analyzed and concluded the product conceptual design features, researched the traditional digital prototyping modeling and simulation theoretical, referenced the qualitative modeling and simulation theoretical, the concept of qualitative digital prototyping that defined from object, function, capability and technology is proposed in this dissertation. An object-oriented framework strategy is used to implement the framework of the qualitative digital prototyping to illustrate the structure of qualitative digital prototyping.
The organic connections and interaction relationships exist between the system elements which recognized in product conceptual design stage, and structural analysis method has advantage in relationship analysis between the system elements, so a qualitative digital prototyping modeling method which based on structural modeling method is proposed in this dissertation. In this method, the interaction relationships between system elements are analyzed and described by structural modeling method first. And second, the relevant constraints which in accordance with the form of algebraic constraint and qualitative constraint in qualitative simulation models are generated between the elements which have direct causal relationships. Third, the all constraints generated in second step are filtered by constraint consistency, behavioral consistency and dimensional consistency. Finally, the qualitative differential equations which can directly use for simulation are generated.
Because of Kuipers Qualitative Simulation method actually has the feature of the constraint satisfaction problems; it can be looked as solving a class of constraint satisfaction problem. In order to improve simulation efficiency and facilitate system modeling, the author use constraint logic programming language improves Kuipers Qualitative Simulation method. According to the constraint solving is based on "first generate and then to examine" principle, the constraint solving ability is added on the basis of the logical language, and it may be make it more suitable for the application of qualitative knowledge and programming.
Because of qualitative simulation method is sufficient and completeness, it easy leads to the simulation contain a large number of redundant result. If the model size relatively large, the matching combination and global consistency filtering will be very difficult. Directed towards those problems two different model decomposition methods which based on strongly connected graph and genetic algorithm are proposed in this dissertation. According to the actual situation of object system, user can select different method to decompose the system model. After accomplish the model decomposition, a new simplified system model which composed by subsystems will be obtained. Each subsystem can simulate separately. Matching combination and redundant behavior filtering are distributed separately in each subsystem, the simulation efficiency will be greatly improved and the difficult of simulation will be reduced.
The ambiguity which caused by qualitative computing will be reduced if we Introduce the quantitative knowledge into qualitative simulation. Synthesized the qualitative and quantitative knowledge can construct the system model at different levels, and more precise define the system and its behavior. In order to reduce the simulation branch of unnecessary, save a lot of unnecessary calculations, improve simulation efficiency, and meet different application requirements, fuzzy-based qualitative and quantitative synthesized simulation method is proposed in this dissertation. Fuzzy-based simulation method provides a method for effective application the quantities knowledge in conceptual design stage.
在对产品概念设计的特点、传统数字样机系统的建立与应用、定性建模与仿真理论分析与总结的基础上,本文从目标、功能、能力、技术组成四个方面对定性数字样机进行定义,并采用面向对象的体系框架构建策略实现了定性数字样机体系框架的构建。
针对概念设计阶段所识别系统组成要素间所具有的有机联系和相互作用关系,以及结构化分析方法在定性知识分析上的优势,本文提出了基于结构模型化的定性数字样机建模方法。通过对系统变量间的相互影响关系进行结构化分析与描述,按照定性仿真模型中的代数约束和定性约束形式在所有具有直接因果影响关系的变量间生成相应的约束形式,并进行约束一致性、行为一致性和量纲一致性过滤,生成可直接用于仿真的定性微分方程。
针对Kuipers定性仿真方法实际具备约束满足类问题特性的特点,将其看着是求解一类约束满足问题,从提高仿真效率、方便仿真建模的角度,将Kuipers定性仿真方法根据约束逻辑程序语言进行改进。根据约束求解是基于“先生成后检验”的原则,在逻辑语言的基础上添加了对约束的求解能力,使之更适合于定性知识的应用和程序实现。
定性仿真由于其充分不完备性,易导致仿真出现大量冗余结果,当模型量比较大时进行配对组合、全局一致性过滤会非常困难,针对这种情况本文提出了基于强连通图和遗传算法的两种模型分解方法,根据对象系统的实际情况,按照不同的方法进行系统模型分解,并对分解之后的子系统按照其在原系统中的位置进行重新组合,构成新的简化系统。分解后的各个子系统单独进行仿真,其输出结果即为与之相连的子系统的输入。模型分解将完整的配对组合和冗余行为过滤分散在各个子系统中分别进行,所以可以极大提高复杂系统的仿真效率、降低仿真难度。
在定性仿真中引入定量知识,能够减少定性仿真中由定性计算导致的模糊性、构造不同层次的系统模型、较为精确的定义系统及其行为,从而减少许多不必要的仿真分支,节省大量无谓的计算,能够很好的提高仿真效率,满足不同的应用需求。本文从扩展系统知识应用的角度,采用模糊数学方法构造定量信息与定性信息相集成的定性仿真算法,实现定性定量知识综合的数字样机。基于模糊数学的定性定量知识结合的仿真方法为有效应用产品概念设计阶段的定量信息提供了有效方法。
Product conceptual design activities play an important role in product life cycle. As effective auxiliary product design and analysis tools, qualitative methods and digital prototyping can extend engineers'design mentality and provide new tools for product innovation. Traditional quantitative digital prototyping is lack of the support of non-number and inaccurate data on reason the system behavior. In fact, there is a large number of qualitative knowledge exist in conceptual design stage. Directed towards those reasons a concept of qualitative digital prototyping is proposed in this dissertation, and some key technical problems are researched.
After the author analyzed and concluded the product conceptual design features, researched the traditional digital prototyping modeling and simulation theoretical, referenced the qualitative modeling and simulation theoretical, the concept of qualitative digital prototyping that defined from object, function, capability and technology is proposed in this dissertation. An object-oriented framework strategy is used to implement the framework of the qualitative digital prototyping to illustrate the structure of qualitative digital prototyping.
The organic connections and interaction relationships exist between the system elements which recognized in product conceptual design stage, and structural analysis method has advantage in relationship analysis between the system elements, so a qualitative digital prototyping modeling method which based on structural modeling method is proposed in this dissertation. In this method, the interaction relationships between system elements are analyzed and described by structural modeling method first. And second, the relevant constraints which in accordance with the form of algebraic constraint and qualitative constraint in qualitative simulation models are generated between the elements which have direct causal relationships. Third, the all constraints generated in second step are filtered by constraint consistency, behavioral consistency and dimensional consistency. Finally, the qualitative differential equations which can directly use for simulation are generated.
Because of Kuipers Qualitative Simulation method actually has the feature of the constraint satisfaction problems; it can be looked as solving a class of constraint satisfaction problem. In order to improve simulation efficiency and facilitate system modeling, the author use constraint logic programming language improves Kuipers Qualitative Simulation method. According to the constraint solving is based on "first generate and then to examine" principle, the constraint solving ability is added on the basis of the logical language, and it may be make it more suitable for the application of qualitative knowledge and programming.
Because of qualitative simulation method is sufficient and completeness, it easy leads to the simulation contain a large number of redundant result. If the model size relatively large, the matching combination and global consistency filtering will be very difficult. Directed towards those problems two different model decomposition methods which based on strongly connected graph and genetic algorithm are proposed in this dissertation. According to the actual situation of object system, user can select different method to decompose the system model. After accomplish the model decomposition, a new simplified system model which composed by subsystems will be obtained. Each subsystem can simulate separately. Matching combination and redundant behavior filtering are distributed separately in each subsystem, the simulation efficiency will be greatly improved and the difficult of simulation will be reduced.
The ambiguity which caused by qualitative computing will be reduced if we Introduce the quantitative knowledge into qualitative simulation. Synthesized the qualitative and quantitative knowledge can construct the system model at different levels, and more precise define the system and its behavior. In order to reduce the simulation branch of unnecessary, save a lot of unnecessary calculations, improve simulation efficiency, and meet different application requirements, fuzzy-based qualitative and quantitative synthesized simulation method is proposed in this dissertation. Fuzzy-based simulation method provides a method for effective application the quantities knowledge in conceptual design stage.
引文
[1]邹慧军,汪利,王石刚,郭为中。机械产品概念设计及其方法综述。机械设计与研究,1998(2)：9-12。
[2]熊光楞,李伯虎,柴旭东。虚拟样机技术。系统仿真学报,2001,13(1)：114-117。
[3]谭建荣,刘振宇。数字样机：关键技术与产品应用。北京：机械工业出版社,2007.5。
[4]石纯,廖士中。定性推理方法。北京：清华大学出版社,2002.9。
[5]http://en.wikipedia.org/wiki/Digital_prototyping
[6]Sturges R. H., Shaughnessy K., Kilani M. J. Computational model for conceptual design based on extended function logic. AI for Engineering Design Analysis and Manufacturing,1996,78(69):11-18.
[7]舒慧林,刘继红,钟毅芳。计算机辅助机械产品概念设计研究综述。计算机辅助设计与图形学学报,2000,12(12)：948-954。
[8]檀润华,陈鹰。产生多个设计方案的键合图方法。机械设计,1998,15(1)：14-16。
[9]Umeda Y, et al. Supporting conceptual design based on the function-behavior-state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing,1996,10(4):275-288.
[10]Gorti S. R., Sriram R. D. From symbol to form:A framework for conceptual design. Computer-Aided Design,1996.
[11]Neville D., Joskowicz L. A representation language for mechanical behavior. Design theory and methodology,1993,85(95):2-6.
[12]Qian L., Gero J. S. Function-Behavior-Structure paths and their role in analogy-based design. AI for Engineering Design Analysis and Manufacturing,1996, 65(36):8-11.
[13]B. J. Kuipers. Qualitative simulation. Artificial Intelligence,1986 29:289-338.
[14]B. J. Kuipers. Qualitative reasoning: modeling and simulation with incomplete knowledge. The MIT Press. Cambridge, Massachusetts. London, England,1994.
[15]De Kleer, Brown J. S. A qualitative physics based on confluences. Artificial Intelligence,1984,24:7-83.
[16]K. Forbus, Qualitative process theory. Artificial Intelligence.1984,24:85-168.
[17]D. Berleant, B. Kuipers, Qualitative and quantitative simulation:bridging the gap. Artificial Intelligence,1997,95:215-255.
[18]张文明,刘怀春,白方周。基于行为的模型自动建立方法。信息与控制,1998,27(3)：179-184。
[19]刘怀春,张文明,白方周。一种化工过程定性约束模型的自动生成方法。计算机与应用化学,1998,15(1)：14-18。
[20]檀润华,王林翔,苑彩云,王泳滨,段国林。基于键合图的动态系统自动符号建模。机械科学与技术,Vol.16, No.4,1997。
[21]檀润华,付晓宇,王泳滨。基于功率键图的计算机建模。河北工学院学报,Vol.23,No.4,1994。
[22]汪旭。基于面向对象方法的模型构造系统。决策与决策支持系统,Vol.7,No.3,1997。
[23]Li Yubing, Chen Guanlong, Lai Xinming. A framework of auto body assembly qualitative simulation system. Int. J Adv Manuf. Technol. (2007)33:1244-1255.
[24]Daniel J. Clancy, Benjamin Kuipers. Model decomposition and simulation:A component based qualitative simulation algorithm. In the 14th National Conference on Artificial Intelligence (AAAI-97),1997.
[25]Y. Iwasaki, H. Simon. Causality in device behavior. Artificial Intelligence,1986, 29(1):3-32.
[26]Liang Chang-yong, Huang Ti-yun, Yang Shan-lin. A decomposing algorithm of the qualitative model. Journal of HeiFei University of Technology,2000,24(2):6-11.
[27]Zhang Wen-ming, Liu Hui-chun, Bai Fang-zhou. A directed-graph-based decomposed method for qualitative models. Computer Research & Development, 1998,35(8):709-713.
[28]Leitch R, Freitag H, Struss P, Tornielle G.ARTIST:A Methodological Approach to Specifying Model Based Diagnostic Systems. Intelligent Automation laboratory, Herriot-Watt University, Edin-burgh & Advanced Reasoning Methods. Siemens A G, Munich, Germany & Artificial Intelligence Section.
[29]Dague Ph, Raiman O, Deves Ph. Trouble-shooting:When Modeling is the Trouble. IBM Scientific Center, Paris, France & Electronique Serge Dassault, France,1987.
[30]Bratko I, Mozetic I, Lavrac N.KARDIO:A Study in Deep and Qualitative Knowledge for Expert Systems. MIT Press,1989.
[31]石莉。定性推理在概念设计中的应用研究[硕士学位论文]。西安电子科技大学,2001。
[32]Michael L. Mavrovouniotis. A belief framework for order-of-magnitude reasoning and other qualitative relations. Artificial Intelligence in Engineering 1997(11):121-134.
[33]Nagib G, Gharieb W., Binder Z. Qualitative multi-model control using a learning approach. Int. J. System Science,1992,23(6):855-869.
[34]MQ & D. Qualitative reasoning: A survey of techniques and application. AICOM, 1995,8:119-192.
[35]Corea R., Tham M. T., Morris A. J. Qualitative modeling-An application to supervisory control of a distillation column. IEEE Int. Symp. On Intelligent Control,1992,586-591.
[36]Fanni A., Giua A. discrete event representation of qualitative models using Petri Nets. IEEE Trans. On Systems, Man and Cyber-part B:Cyber,1998,28(6): 770-780.
[37]Lunze J. Stabilization of nonlinear systems by qualitative feedback controllers. Int. J. Control,1995,62(1):109-128.
[38]Urban, Bratko I. Constructing control rules for a dynamic system:probabilistic qualitative models, look ahead and exaggeration. Int. J. System Science,1992, 24(6):1155-1164.
[39]Shen Q., Leitch R. Fuzzy qualitative simulation. IEEE Trans. On Sys Man & Cyber, 1993,23(4):1038-1061.
[40]Fishwick P. A., Lucker P. A. Qualitative simulation, modeling and analysis. Spring-Verlag, New York,1991.
[41]Y. Iwasaki, H. Simon. Causality in device behavior. Artificial Intelligence,1986, 29(1):3-32.
[42]M. D. Singh, R. Kant. Knowledge management barriers:An interpretive structural modeling approach. International Journal of Management Science and Engineering Management Vol.3 No.2,2008, pp:141-150.
[43]李余兵。基于定性仿真的车身概念装配工艺规划与设计决策研究[博士学位论文]。上海交通大学,2007.
[44]Michael Van Wie, Robert Stone, Asli Sabin, etc. A qualitative modeling method for platform design. IEEE 2005 312-317.
[45]石峰,吴东坡,李群等。面向复杂仿真因果追溯的行为建模方法。系统仿真学报,2007,19(2)：405-409。
[46]白方周,张雷。定性仿真导论。合肥：中国科学技术出版社,1998.10。
[47]Laszlo Teleki. Embedding qualitative reasoning into constraint logic programming. In Proceedings of the Workshop on Constraint Programming Applications, Cambridge, Massachusetts, USA, August,1996.
[48]王洪强。基于模糊集理论的动态系统定型建模方法研究及实现。长沙：国防科 技大学,2000.
[49]王东锋。模糊定性仿真理论研究与实现。广东：国防科技大学,1999.
[50]王东锋,陈英武。模糊定性建模与仿真集成实现。系统工程理论与实践,2002,3：119-124.
[51]白方周,鲍忠贵等。可信度增强的模糊定性仿真。自动化学报,1998,24(1)：147-151.
[52]B. J. Kuipers and D. Berlant. Using incomplete quantitative knowledge in qualitative reasoning. Proceedings AAAI-88, St. Paul. MN (1988):324-329.
[53]朱六璋,陈宗海。复杂系统的定性建模和定性控制综述。信息与控制,2001,30(3)：234-239.
[54]Lunze J. On the stability qualitative models system & control letters,1993,21: 137-142.
[55]Gerard Howe, Andrew McKay. Combining quantitative and qualitative methods in assessing chronic poverty:The case of Rwanda. World Development Vol.35, No.2, 2007, pp:197-211.
[56]K. Eisenack, H. Welsch, J. P. Kropp. A qualitative dynamical modeling approach to capital accumulation in unregulated fisheries. Journal of Economic Dynamics & Control 30(2006)2613-2636.
[57]H. A. Simon. The science of the artificial [M]. Cambridge, MA:MIT Press,1969.
[58]D. Berleant and B. Kuipers, Combined qualitative and numerical simulation with Q3. Proceedings Workshop on Qualitative Physics, Lugano, Switzerland (1990): p140-152.
[59]邵维忠,杨芙清。面向对象的系统设计。北京：清华大学出版社,2003.2。
[60]屠仁寿,李伯虎,王正中。当前仿真方法学发展中存在的问题。系统仿真学报,No.2,1998。
[61]Cen Say A. C., Selahattin Kuru. Qualitative system identification:deriving structure from behavior. Artificial Intelligence,1996,83:75-141.
[62]陈英武.定性推理：一种新的建模仿真方法。系统工程,1994年5月第12卷第3期：P49-55。
[63]Clark P, Renken K and Stule N. MED:An object oriented simulation environment. TASC, Fairbom, Ohio [M], USA.1993.
[64]Balci O, et al. The visual simulation environment. In Proc the 11th European [M]. Mulaiconf, Istanbul, Turkey, June,1997.
[65]张晶,曾宪云,曾岳南。计算机仿真新技术。微型电脑应用,2002年第18卷 第2期。
[66]George J. Klir, Aspects of University in Qualitative Systems Modeling, Qualitative Simulation Modeling and Analysis, Springer_Verlag, New York, inc.,1991.
[67]Cellier, F.E. General System problem solving paradigm for qualitative modeling. In: Advances in simulation Vol. V: Qualitative simulation modeling and analysis, edited by P. A. Luker and P. A. Fishwick. Springer-Verlag, New York,1991.
[68]Dekleer. Causality in device behavior. Artificial Intelligence 59 (1993).
[69]Zadeh, L.A. A computational theory of disposition. Int. J. Intell. Syst. Vol.2(1987), 39-63.
[70]Zadeh, L.A. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispostions. IEEE Trans. Syst., Man, Cybern. SMC Vol.15, No6 (1985),754-763.
[71]Klaus Eisenack, Gerhard Petschel-Held. Graph Theoretical Analysis of Qualitative Models in Sustainbility Science. In Proceedings of Sixteenth International Workshop on Qualitative Reasoning,10-12,2002.
[72]Cis Schut, Bert Bredfwdg. An overview of Approaches to qualitative model construction. The Knowledge Engineering Review.1996,11:PI-25.
[73]Yumi Iwasaki, Causal Ordering Analysis, In:Paul A. Fishwick ed. Qualitative Simulation Modeling and Analysis, Ch.5, New York:Springer-Verlag,1991, 98-119.
[74]D. Berleant and B. Kuipers, Combined qualitative and numerical simulation with Q3. Proceedings Workshop on Qualitative Physics, Lugano, Switzerland (1990): p140-152.
[75]B. J. Kuipers and D. Berlant. Using incomplete quantitative knowledge in qualitative reasoning. Proceedings AAAI-88, St. Paul. MN (1988):p324-329.
[76]K. D. Forbus and B. Falkenhainer, Self-explanatory simulations:An integration of quantitative knowledge. Proceedings AAAI-90, Boston. MA (1990):p380-387.
[77]HAM Danniels, A J Feelders. Combining qualitative and quantitative methods for models for model based Diagnosis of firms.1991, institute of language technology and AI, Tilburge University Neth-lands (decision support system and qualitative reasoning Toulouse, France).
[78]B. C. Williams, A theory of interaction:unifying qualitative and quantitative algebraic reasoning, Aritf. Intell.1991, Vol51:P39-94
[79]Benjamin Kuipers, A Hierarchy of qualitative representations for space.1996.
[80]Ranman Rajagopalan, A model for integrated qualitative spatial and dynamic reasoning about physical systems. In Proceedings of the Twelfth National Conference on Artificial Intelligence,1994.
[81]Wood W. Lee, Benjamin J. Kuipers. A qualitative method to construct phase portraits. In Proceedings of the Eleventh National Conference on Artificial Intelligence,1993.
[82]K. Eisenack, H. Welsch, J.P. Kropp. A qualitative dynamical modeling approach to capital accumulation in unregulated fisheries. Journal of Economic Dynamics & Control 30 (2006) 613-2636.
[83]Wei Guan. A qualitative model of cross-lane inhomogeneities in traffic flow. IEEE Transactions on intelligent transportation systems, VOL.5, NO.3, September 2004:188-199.
[84]Ronald A. Hess. A qualitative model of human interaction with complex dynamic systems. IEEE Transactions on Systems, Man, and Cybernetics, VOL. SMC-17, NO.1, January/February 1987:33-51.
[85]Oren T. GEST-A Modeling and Simulation Language Based on System Theoretic Concepts in Simulation and Model-based Methodologies:An Integrative View. New York,1984.
[86]肖田元,张燕云,陈家栋编著。系统仿真导论。北京：清华大学出版社,2000年7月的1版,3-11.
[87]刘兴堂,吴晓燕编著。现代系统建模与仿真技术。西安：西北工业大学出版社,2001年8月第1版,1-16.
[88]黄柯棣等编著。系统仿真技术。长沙：国防科技大学出版社,1998。
[89]李伯虎,柴旭东等。现代建模与仿真技术发展中的几个焦点。系统仿真学报,2004年9月第16卷第9期：P1871-1878.
[90]Clark P, Renken K and Stule N. MED:An object oriented simulation environment. TASC, Fairbom, Ohio, USA.1993.
[91]Balci O, et al. The visual simulation environment. In Proc the 11th European. Mulaiconf, Istanbul, Turkey, June,1997.
[92]王子才。关于仿真理论的探讨。系统仿真学报,2000年第12卷第5期.
[93]屠仁寿,王正中。而向信息时代的系统仿真方法学。系统仿真学报,1999年第11卷第5期.
[94]Oren, Tuncer Ⅰ。人工智能与仿真。专辑3.《计算机工程与设计》编辑部,1987.
[95]Clark P, Renken K and Stule N. MED:An object oriented simulation environment. TASC, Fairbom, Ohio, USA.1993.
[96]周海英,陈立潮。智能技术在仿真平台设计中的应用。计算机工程,2005年5 月第31卷第9期：P176-178.
[97]MQ&D. Qualitative Reasoning:A Survey of Techniques and Applications. AICOM, 1995,8(3-4):119-192.
[98]李凡。人工智能中的不确定性。北京：气象出版社.1992.
[99]George J. Klir, Aspects of University in Qualitative Systems Modeling, Qualitative Simulation Modeling and Analysis, Springer_Verlag, New York, inc.,1991.
[100]陈贻源。模糊数学。武汉：华中工学院出版社,1984.
[101]Klir G J. The general system as a methodological tool. General System Yearbook, 1965,10:29-42.
[102]Klir G J. An approach to general system theory. New York:Van Nostrand Reinhold, 1969.
[103]Dekleer. Causality in device behavior. Artificial Intelligence 59 (1993).
[104]Zadeh, L. A. A computational theory of disposition. Int. J. Intell. Syst. Vol.2(1987), 39-63.
[105]Zadeh, L.A. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispostions. IEEE Trans. Syst., Man, Cybern. SMC Vol.15, No6 (1985),754-763.
[106]Klaus Eisenack, Gerhard Petschel-Held. Graph Theoretical Analysis of Qualitative Models in Sustainbility Science. In Proceedings of Sixteenth International Workshop on Qualitative Reasoning,10-12,2002.
[107]Kuipers B.J. Qualitative reasoning:Modeling and simulation with incomplete knowledge, Automatics,1989,25,571-585.
[108]白方周,霍鑫,鲍忠贵。动态系统的定性推理：定性建模和定性仿真.信息与控制,1995年第24卷第4期,P222-229.
[109]Dalle Molle D.T. Kuipers B.J. and Edgar T.F. Qualitative modeling and simulation of dynamic systems. Comput. Chem. Engng.1988,12,853-866.
[110]白方周,陈源,鲍忠贵.一个基于QSIM算法的定性仿真系统GQSS.信息与控制.1996年第25卷第1期,P49-57.
[111]Cis Schut, Bert Bredfwdg. An overview of Approaches to qualitative model construction. The Knowledge Engineering Review.1996,11:P1-25.
[112]Cellier, F. E. Qualitative simulation of technical systems using the general system problem solving framework. Int. J. Gen. Sys. Vol.13, No.4 (1987),333-344.
[113]Law, A. M., and Kelton, W. D. Simulation modeling and analysis. McGraw-Hill, New York,1982.
[114]Forrester, J. W. World dynamics. Wright-Allen Press, Cambridge, Massachusetts, 1971.
[115]Yumi Iwasaki, Causal Ordering Analysis, In:Paul A. Fishwick ed. Qualitative Simulation Modeling and Analysis, Ch.5, New York:Springer-Verlag,1991, 98-119.
[116]Amruth N. Kumar, Shambhu J. Upadhyaya. Component-onto logical representation of function for reasoning about devices. Artificial Intelligence in Engineering.1998, 12,:P399-415.
[117]M.Staroswiecki. Quantitative and qualitative models for fault and isolation. Mechanical Systems and Signal Processing,2000,14(3):301-325.
[118]Edward P.Gatzke, Francis J. Doyle. Use of multiple models and qualitative knowledge for on-line moving horizon disturbance estimation and fault diagnosis. Journal of Process Control, February,2002,12(2):339-352.
[119]Marjan Druzovec, Tatjana Welzer, Matjaz Colnaric, Jozsef Gyorkos. Diagnostics and a qualitative model. International Journal of Medical Informatics, September, 2001,63(1-2):51-60.
[120]Andrea Bonarini, Vittorio Maniezzo, Integrating Qualitative and Quantitative Modeling, International Journal of Expert Systems, Vol4,1991,1-15.
[121]D. Berleant and B. Kuipers, Combined qualitative and numerical simulation with Q3. Proceedings Workshop on Qualitative Physics, Lugano, Switzerland (1990): p140-152.
[2]熊光楞,李伯虎,柴旭东。虚拟样机技术。系统仿真学报,2001,13(1)：114-117。
[3]谭建荣,刘振宇。数字样机：关键技术与产品应用。北京：机械工业出版社,2007.5。
[4]石纯,廖士中。定性推理方法。北京：清华大学出版社,2002.9。
[5]http://en.wikipedia.org/wiki/Digital_prototyping
[6]Sturges R. H., Shaughnessy K., Kilani M. J. Computational model for conceptual design based on extended function logic. AI for Engineering Design Analysis and Manufacturing,1996,78(69):11-18.
[7]舒慧林,刘继红,钟毅芳。计算机辅助机械产品概念设计研究综述。计算机辅助设计与图形学学报,2000,12(12)：948-954。
[8]檀润华,陈鹰。产生多个设计方案的键合图方法。机械设计,1998,15(1)：14-16。
[9]Umeda Y, et al. Supporting conceptual design based on the function-behavior-state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing,1996,10(4):275-288.
[10]Gorti S. R., Sriram R. D. From symbol to form:A framework for conceptual design. Computer-Aided Design,1996.
[11]Neville D., Joskowicz L. A representation language for mechanical behavior. Design theory and methodology,1993,85(95):2-6.
[12]Qian L., Gero J. S. Function-Behavior-Structure paths and their role in analogy-based design. AI for Engineering Design Analysis and Manufacturing,1996, 65(36):8-11.
[13]B. J. Kuipers. Qualitative simulation. Artificial Intelligence,1986 29:289-338.
[14]B. J. Kuipers. Qualitative reasoning: modeling and simulation with incomplete knowledge. The MIT Press. Cambridge, Massachusetts. London, England,1994.
[15]De Kleer, Brown J. S. A qualitative physics based on confluences. Artificial Intelligence,1984,24:7-83.
[16]K. Forbus, Qualitative process theory. Artificial Intelligence.1984,24:85-168.
[17]D. Berleant, B. Kuipers, Qualitative and quantitative simulation:bridging the gap. Artificial Intelligence,1997,95:215-255.
[18]张文明,刘怀春,白方周。基于行为的模型自动建立方法。信息与控制,1998,27(3)：179-184。
[19]刘怀春,张文明,白方周。一种化工过程定性约束模型的自动生成方法。计算机与应用化学,1998,15(1)：14-18。
[20]檀润华,王林翔,苑彩云,王泳滨,段国林。基于键合图的动态系统自动符号建模。机械科学与技术,Vol.16, No.4,1997。
[21]檀润华,付晓宇,王泳滨。基于功率键图的计算机建模。河北工学院学报,Vol.23,No.4,1994。
[22]汪旭。基于面向对象方法的模型构造系统。决策与决策支持系统,Vol.7,No.3,1997。
[23]Li Yubing, Chen Guanlong, Lai Xinming. A framework of auto body assembly qualitative simulation system. Int. J Adv Manuf. Technol. (2007)33:1244-1255.
[24]Daniel J. Clancy, Benjamin Kuipers. Model decomposition and simulation:A component based qualitative simulation algorithm. In the 14th National Conference on Artificial Intelligence (AAAI-97),1997.
[25]Y. Iwasaki, H. Simon. Causality in device behavior. Artificial Intelligence,1986, 29(1):3-32.
[26]Liang Chang-yong, Huang Ti-yun, Yang Shan-lin. A decomposing algorithm of the qualitative model. Journal of HeiFei University of Technology,2000,24(2):6-11.
[27]Zhang Wen-ming, Liu Hui-chun, Bai Fang-zhou. A directed-graph-based decomposed method for qualitative models. Computer Research & Development, 1998,35(8):709-713.
[28]Leitch R, Freitag H, Struss P, Tornielle G.ARTIST:A Methodological Approach to Specifying Model Based Diagnostic Systems. Intelligent Automation laboratory, Herriot-Watt University, Edin-burgh & Advanced Reasoning Methods. Siemens A G, Munich, Germany & Artificial Intelligence Section.
[29]Dague Ph, Raiman O, Deves Ph. Trouble-shooting:When Modeling is the Trouble. IBM Scientific Center, Paris, France & Electronique Serge Dassault, France,1987.
[30]Bratko I, Mozetic I, Lavrac N.KARDIO:A Study in Deep and Qualitative Knowledge for Expert Systems. MIT Press,1989.
[31]石莉。定性推理在概念设计中的应用研究[硕士学位论文]。西安电子科技大学,2001。
[32]Michael L. Mavrovouniotis. A belief framework for order-of-magnitude reasoning and other qualitative relations. Artificial Intelligence in Engineering 1997(11):121-134.
[33]Nagib G, Gharieb W., Binder Z. Qualitative multi-model control using a learning approach. Int. J. System Science,1992,23(6):855-869.
[34]MQ & D. Qualitative reasoning: A survey of techniques and application. AICOM, 1995,8:119-192.
[35]Corea R., Tham M. T., Morris A. J. Qualitative modeling-An application to supervisory control of a distillation column. IEEE Int. Symp. On Intelligent Control,1992,586-591.
[36]Fanni A., Giua A. discrete event representation of qualitative models using Petri Nets. IEEE Trans. On Systems, Man and Cyber-part B:Cyber,1998,28(6): 770-780.
[37]Lunze J. Stabilization of nonlinear systems by qualitative feedback controllers. Int. J. Control,1995,62(1):109-128.
[38]Urban, Bratko I. Constructing control rules for a dynamic system:probabilistic qualitative models, look ahead and exaggeration. Int. J. System Science,1992, 24(6):1155-1164.
[39]Shen Q., Leitch R. Fuzzy qualitative simulation. IEEE Trans. On Sys Man & Cyber, 1993,23(4):1038-1061.
[40]Fishwick P. A., Lucker P. A. Qualitative simulation, modeling and analysis. Spring-Verlag, New York,1991.
[41]Y. Iwasaki, H. Simon. Causality in device behavior. Artificial Intelligence,1986, 29(1):3-32.
[42]M. D. Singh, R. Kant. Knowledge management barriers:An interpretive structural modeling approach. International Journal of Management Science and Engineering Management Vol.3 No.2,2008, pp:141-150.
[43]李余兵。基于定性仿真的车身概念装配工艺规划与设计决策研究[博士学位论文]。上海交通大学,2007.
[44]Michael Van Wie, Robert Stone, Asli Sabin, etc. A qualitative modeling method for platform design. IEEE 2005 312-317.
[45]石峰,吴东坡,李群等。面向复杂仿真因果追溯的行为建模方法。系统仿真学报,2007,19(2)：405-409。
[46]白方周,张雷。定性仿真导论。合肥：中国科学技术出版社,1998.10。
[47]Laszlo Teleki. Embedding qualitative reasoning into constraint logic programming. In Proceedings of the Workshop on Constraint Programming Applications, Cambridge, Massachusetts, USA, August,1996.
[48]王洪强。基于模糊集理论的动态系统定型建模方法研究及实现。长沙：国防科 技大学,2000.
[49]王东锋。模糊定性仿真理论研究与实现。广东：国防科技大学,1999.
[50]王东锋,陈英武。模糊定性建模与仿真集成实现。系统工程理论与实践,2002,3：119-124.
[51]白方周,鲍忠贵等。可信度增强的模糊定性仿真。自动化学报,1998,24(1)：147-151.
[52]B. J. Kuipers and D. Berlant. Using incomplete quantitative knowledge in qualitative reasoning. Proceedings AAAI-88, St. Paul. MN (1988):324-329.
[53]朱六璋,陈宗海。复杂系统的定性建模和定性控制综述。信息与控制,2001,30(3)：234-239.
[54]Lunze J. On the stability qualitative models system & control letters,1993,21: 137-142.
[55]Gerard Howe, Andrew McKay. Combining quantitative and qualitative methods in assessing chronic poverty:The case of Rwanda. World Development Vol.35, No.2, 2007, pp:197-211.
[56]K. Eisenack, H. Welsch, J. P. Kropp. A qualitative dynamical modeling approach to capital accumulation in unregulated fisheries. Journal of Economic Dynamics & Control 30(2006)2613-2636.
[57]H. A. Simon. The science of the artificial [M]. Cambridge, MA:MIT Press,1969.
[58]D. Berleant and B. Kuipers, Combined qualitative and numerical simulation with Q3. Proceedings Workshop on Qualitative Physics, Lugano, Switzerland (1990): p140-152.
[59]邵维忠,杨芙清。面向对象的系统设计。北京：清华大学出版社,2003.2。
[60]屠仁寿,李伯虎,王正中。当前仿真方法学发展中存在的问题。系统仿真学报,No.2,1998。
[61]Cen Say A. C., Selahattin Kuru. Qualitative system identification:deriving structure from behavior. Artificial Intelligence,1996,83:75-141.
[62]陈英武.定性推理：一种新的建模仿真方法。系统工程,1994年5月第12卷第3期：P49-55。
[63]Clark P, Renken K and Stule N. MED:An object oriented simulation environment. TASC, Fairbom, Ohio [M], USA.1993.
[64]Balci O, et al. The visual simulation environment. In Proc the 11th European [M]. Mulaiconf, Istanbul, Turkey, June,1997.
[65]张晶,曾宪云,曾岳南。计算机仿真新技术。微型电脑应用,2002年第18卷 第2期。
[66]George J. Klir, Aspects of University in Qualitative Systems Modeling, Qualitative Simulation Modeling and Analysis, Springer_Verlag, New York, inc.,1991.
[67]Cellier, F.E. General System problem solving paradigm for qualitative modeling. In: Advances in simulation Vol. V: Qualitative simulation modeling and analysis, edited by P. A. Luker and P. A. Fishwick. Springer-Verlag, New York,1991.
[68]Dekleer. Causality in device behavior. Artificial Intelligence 59 (1993).
[69]Zadeh, L.A. A computational theory of disposition. Int. J. Intell. Syst. Vol.2(1987), 39-63.
[70]Zadeh, L.A. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispostions. IEEE Trans. Syst., Man, Cybern. SMC Vol.15, No6 (1985),754-763.
[71]Klaus Eisenack, Gerhard Petschel-Held. Graph Theoretical Analysis of Qualitative Models in Sustainbility Science. In Proceedings of Sixteenth International Workshop on Qualitative Reasoning,10-12,2002.
[72]Cis Schut, Bert Bredfwdg. An overview of Approaches to qualitative model construction. The Knowledge Engineering Review.1996,11:PI-25.
[73]Yumi Iwasaki, Causal Ordering Analysis, In:Paul A. Fishwick ed. Qualitative Simulation Modeling and Analysis, Ch.5, New York:Springer-Verlag,1991, 98-119.
[74]D. Berleant and B. Kuipers, Combined qualitative and numerical simulation with Q3. Proceedings Workshop on Qualitative Physics, Lugano, Switzerland (1990): p140-152.
[75]B. J. Kuipers and D. Berlant. Using incomplete quantitative knowledge in qualitative reasoning. Proceedings AAAI-88, St. Paul. MN (1988):p324-329.
[76]K. D. Forbus and B. Falkenhainer, Self-explanatory simulations:An integration of quantitative knowledge. Proceedings AAAI-90, Boston. MA (1990):p380-387.
[77]HAM Danniels, A J Feelders. Combining qualitative and quantitative methods for models for model based Diagnosis of firms.1991, institute of language technology and AI, Tilburge University Neth-lands (decision support system and qualitative reasoning Toulouse, France).
[78]B. C. Williams, A theory of interaction:unifying qualitative and quantitative algebraic reasoning, Aritf. Intell.1991, Vol51:P39-94
[79]Benjamin Kuipers, A Hierarchy of qualitative representations for space.1996.
[80]Ranman Rajagopalan, A model for integrated qualitative spatial and dynamic reasoning about physical systems. In Proceedings of the Twelfth National Conference on Artificial Intelligence,1994.
[81]Wood W. Lee, Benjamin J. Kuipers. A qualitative method to construct phase portraits. In Proceedings of the Eleventh National Conference on Artificial Intelligence,1993.
[82]K. Eisenack, H. Welsch, J.P. Kropp. A qualitative dynamical modeling approach to capital accumulation in unregulated fisheries. Journal of Economic Dynamics & Control 30 (2006) 613-2636.
[83]Wei Guan. A qualitative model of cross-lane inhomogeneities in traffic flow. IEEE Transactions on intelligent transportation systems, VOL.5, NO.3, September 2004:188-199.
[84]Ronald A. Hess. A qualitative model of human interaction with complex dynamic systems. IEEE Transactions on Systems, Man, and Cybernetics, VOL. SMC-17, NO.1, January/February 1987:33-51.
[85]Oren T. GEST-A Modeling and Simulation Language Based on System Theoretic Concepts in Simulation and Model-based Methodologies:An Integrative View. New York,1984.
[86]肖田元,张燕云,陈家栋编著。系统仿真导论。北京：清华大学出版社,2000年7月的1版,3-11.
[87]刘兴堂,吴晓燕编著。现代系统建模与仿真技术。西安：西北工业大学出版社,2001年8月第1版,1-16.
[88]黄柯棣等编著。系统仿真技术。长沙：国防科技大学出版社,1998。
[89]李伯虎,柴旭东等。现代建模与仿真技术发展中的几个焦点。系统仿真学报,2004年9月第16卷第9期：P1871-1878.
[90]Clark P, Renken K and Stule N. MED:An object oriented simulation environment. TASC, Fairbom, Ohio, USA.1993.
[91]Balci O, et al. The visual simulation environment. In Proc the 11th European. Mulaiconf, Istanbul, Turkey, June,1997.
[92]王子才。关于仿真理论的探讨。系统仿真学报,2000年第12卷第5期.
[93]屠仁寿,王正中。而向信息时代的系统仿真方法学。系统仿真学报,1999年第11卷第5期.
[94]Oren, Tuncer Ⅰ。人工智能与仿真。专辑3.《计算机工程与设计》编辑部,1987.
[95]Clark P, Renken K and Stule N. MED:An object oriented simulation environment. TASC, Fairbom, Ohio, USA.1993.
[96]周海英,陈立潮。智能技术在仿真平台设计中的应用。计算机工程,2005年5 月第31卷第9期：P176-178.
[97]MQ&D. Qualitative Reasoning:A Survey of Techniques and Applications. AICOM, 1995,8(3-4):119-192.
[98]李凡。人工智能中的不确定性。北京：气象出版社.1992.
[99]George J. Klir, Aspects of University in Qualitative Systems Modeling, Qualitative Simulation Modeling and Analysis, Springer_Verlag, New York, inc.,1991.
[100]陈贻源。模糊数学。武汉：华中工学院出版社,1984.
[101]Klir G J. The general system as a methodological tool. General System Yearbook, 1965,10:29-42.
[102]Klir G J. An approach to general system theory. New York:Van Nostrand Reinhold, 1969.
[103]Dekleer. Causality in device behavior. Artificial Intelligence 59 (1993).
[104]Zadeh, L. A. A computational theory of disposition. Int. J. Intell. Syst. Vol.2(1987), 39-63.
[105]Zadeh, L.A. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispostions. IEEE Trans. Syst., Man, Cybern. SMC Vol.15, No6 (1985),754-763.
[106]Klaus Eisenack, Gerhard Petschel-Held. Graph Theoretical Analysis of Qualitative Models in Sustainbility Science. In Proceedings of Sixteenth International Workshop on Qualitative Reasoning,10-12,2002.
[107]Kuipers B.J. Qualitative reasoning:Modeling and simulation with incomplete knowledge, Automatics,1989,25,571-585.
[108]白方周,霍鑫,鲍忠贵。动态系统的定性推理：定性建模和定性仿真.信息与控制,1995年第24卷第4期,P222-229.
[109]Dalle Molle D.T. Kuipers B.J. and Edgar T.F. Qualitative modeling and simulation of dynamic systems. Comput. Chem. Engng.1988,12,853-866.
[110]白方周,陈源,鲍忠贵.一个基于QSIM算法的定性仿真系统GQSS.信息与控制.1996年第25卷第1期,P49-57.
[111]Cis Schut, Bert Bredfwdg. An overview of Approaches to qualitative model construction. The Knowledge Engineering Review.1996,11:P1-25.
[112]Cellier, F. E. Qualitative simulation of technical systems using the general system problem solving framework. Int. J. Gen. Sys. Vol.13, No.4 (1987),333-344.
[113]Law, A. M., and Kelton, W. D. Simulation modeling and analysis. McGraw-Hill, New York,1982.
[114]Forrester, J. W. World dynamics. Wright-Allen Press, Cambridge, Massachusetts, 1971.
[115]Yumi Iwasaki, Causal Ordering Analysis, In:Paul A. Fishwick ed. Qualitative Simulation Modeling and Analysis, Ch.5, New York:Springer-Verlag,1991, 98-119.
[116]Amruth N. Kumar, Shambhu J. Upadhyaya. Component-onto logical representation of function for reasoning about devices. Artificial Intelligence in Engineering.1998, 12,:P399-415.
[117]M.Staroswiecki. Quantitative and qualitative models for fault and isolation. Mechanical Systems and Signal Processing,2000,14(3):301-325.
[118]Edward P.Gatzke, Francis J. Doyle. Use of multiple models and qualitative knowledge for on-line moving horizon disturbance estimation and fault diagnosis. Journal of Process Control, February,2002,12(2):339-352.
[119]Marjan Druzovec, Tatjana Welzer, Matjaz Colnaric, Jozsef Gyorkos. Diagnostics and a qualitative model. International Journal of Medical Informatics, September, 2001,63(1-2):51-60.
[120]Andrea Bonarini, Vittorio Maniezzo, Integrating Qualitative and Quantitative Modeling, International Journal of Expert Systems, Vol4,1991,1-15.
[121]D. Berleant and B. Kuipers, Combined qualitative and numerical simulation with Q3. Proceedings Workshop on Qualitative Physics, Lugano, Switzerland (1990): p140-152.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |