摘要
本文研究了模糊智能系统的建模过程,并结合模糊逻辑、神经网络和知识工程等理论和方法,提出了一种利用模糊知识建立智能系统的可行途径,并依照此思想设计和实现了基于模糊知识的智能系统开发环境。本文从知识表示和推理机制两个方面阐述了智能系统开发环境的设计和实现。
知识表示方面。实现了知识的异构表示。在知识获取模块,知识表示采用了基于XML的模糊知识框架表示方法;在推理模块,把XML框架解析为JAVA知识对象。这两种知识形态的存在使得系统在存储时具有可读性、用户友好性、平台无关性和网络传输性;在推理时具有封装性、智能性和处理速度快等特点。
推理方面。扩展了传统的Mamdani推理模型,可以同时对多种模糊知识(精确值、隶属函数、语言值等)进行匹配推理;在推理控制策略上选择了反向推理和回溯算法;采用了模糊变量的语义距离来计算模糊变量之间的匹配程度;同时设计了语气算子处理模块,使得系统推理更加符合人类的思维习惯。
隶属函数生成器。采用模糊C-均值聚类算法对模糊变量的样本值进行分类,利用基于Levenberg—Marquardt优化算法的神经网络实现隶属函数的生成,并把学习结果导入XML知识文件。
传统的智能系统开发工具大都采用命令行的方式,设计了窗口命令的方式实现与用户之间的交互,这降低了系统的使用难度,增强了系统的透明度。
Based on researching the modeling of fuzzy intelligent system, combined with such theories and methods as fuzzy logic, neutral network, knowledge engineering and so on, a practicable approach of establishing an intelligent system making use of fuzzy knowledge is presented in this paper and according to this idea, a fuzzy knowledge based environment of intelligent system is designed and implemented. It is from two aspects of both knowledge representation and reasoning mechanism that the design and implementation of the environment are detailed.
On the knowledge representation, isomerous representation of knowledge is realized. In knowledge-acquiring module, the notation of fuzzy knowledge frame based on XML is adopted; in reasoning module, the XML frame is parsed into JAVA knowledge objects. The existence of these two forms of knowledge makes system knowledge readable, friendly, platform-independent and network-transmittable when stored, and meanwhile makes it encapsulated, intelligent, quick-processing and etc. when reasoned.
On the reasoning, the traditional reasoning model of Mamdani is expanded and various kinds of fuzzy knowledge can be matched and reasoned at one time; on the control strategy of reasoning, reverse reasoning and trace algorithm are selected; semantic distance of fuzzy variable is used to calculate the matching extent between fuzzy variables; at the same time, a processing module of mood operator is designed, making the reasoning system more accordant to human thinking custom.
On the member function generator, the sampling values of fuzzy variable are classified by the algorithm of fuzzy C-mean clustering, the member functions are generated through BP network based on the L-M optimized algorithm and the learning results are imported into files of XML language.
In stead of using the conventional method of command line adopted by most intelligent developing tools, the method of window command is designed to achieve the intercourse with
users, which reduces using difficulty and enhances the transparency of the system.
引文
[1] 林尧瑞,马少平.人工智能导论. 北京:清华大学出版社,1989
[2] McRobbie M A and Siekmann J H. Aritificial Intelligence Perspectives and Predictions. Applied Artificial Intelligence, 1991,4:187~207,
[3] 高济. 基于知识的软件智能化技术. 杭州:浙江大学出版社,1999
[4] 张智星,孙春在,[日]水谷英二.神经-模糊和软计算. 西安:西安交通大学出版社, 2000,06
[5] 胡运发编著. 数据与知识工程导论. 北京:清华大学出版社, 2003
[6] 金志权, 陈珮珮编写. 人工智能程序设计-LISP和Prolog. 南京:南京大学出版社,1988
[7] W.F.Clocksin,C.S.Mellish著. 李德毅,赵立平译. PROLOG程序设计. 长沙:国防工业出版社
[8] L.A.Zadeh. Quantitative fuzzy semantics. Information Sciences, 1971,3:159~176
[9] L.A.Zadeh。Fuzzy sets. Information and Control,1965,8:338~335
[10] 窦振中.模糊逻辑控制技术及其应用.北京:北京航空航天大学出版社,1997
[11] 蔡自兴等. 人工智能及其应用(第二版).北京:清华大学出版社, 1996,5
[12] 周治震. 基于模糊技术的设计型专家系统开发平台:[学位论文]. 武汉:华中科技大学, 2002
[13] 吴沁奕. 面向对象模糊专家系统外壳的研制:[学位论文]. 北京:北京理工大学,2002
[14] Woods W A. Important Issues in Knowledge Representation. Proceeding of the IEEE, 1986,10(74)
[15] 李凡. 模糊专家系统. 武汉:华中理工大学出版社, 1995
[16] 何新贵. 模糊知识处理的理论和技术. 北京:国防工业大学出版社, 1994
[17] 刘有才等. 模糊专家系统原理与设计. 北京:北京航空航天大学出版社,1994
[18] 吴正龙,熊范纶等.一个智能知识获取界面的设计与实现.中国科学技术大学学报.2003,2(33) :227~231
[19] Shneider B. Designing the User Interface,3rd edition.MA: Addison-Wesley, Reading,MA,1988
[20] [日]沟口理一郎,石田亨编. 人工智能. 北京:科学出版社,2003
[21] Brett Mclaugblin著. 李二勇,祁力译 .Java与XML数据绑定. 中国电力出版社,2002
[22] 王士同编著. 神经模糊系统及其应用. 北京:北京航空航天大学出版社,1997,08
[23] 胡海静, 王育平等编著. XML技术精粹. 北京:机械工业出版社, 2001
[24] (法)Yves Savourel著李二勇等译. XML国际化和本地化开发.北京:机械工业出版社, 2002.1
[25] Ahmad Lotfi.Learning Fuzzy Inference Systems[Doctor Thesis]Department of Electrical and Computer Engineering,University of Queensland,Australia. 1995,8
[26] Kosko B.Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence.Prentice-Hall,1992
[27] Lin C.T. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall,1996
[28] Joseph Giarratano, Gary Riley著.印鉴,刘星成,汤庸译. 专家系统原理与编程. 北京:机械工业出版社, 2000
[29] 高济.基于知识的软件智能化技术. 杭州:浙江大学出版社,1999
[30] Qihao Chen, Shin Kawase. On fuzzy-valued Fuzzy Reasoning. Fuzzy Sets and Systems, 2000,11(3):237~251
[31] Hisao Ishibuchi, Tomoharu Nakashima,Takehiko Morisawa. Voting in fuzzy rule-based system for pattern classification problems. Fuzzy Sets and systems.1999,103:223~238
[32] John H.Bradley,Richaid.Hauser. A Framework for Expert System Implementation. Expert Systems With applications, 1995, 8(1):157~167
[33] Kosko B. Neural Networks and Fuzzy Systems. London:Prentice-Hall, Inc.,1992
[34] Bruce Eckel. Java编程思想[M]. 北京:机械工业出版社, 2000.150~171
[35] I.B.Turksen. Interval valued fuzzy sets based on normal forms. Fuzzy Sets and System, 1986,20:191~210
[36] 孙兆林,汪东,王鹏译. Java与XML. 北京:中国电力出版社, 2001.11~101