基于遗传算法的神经模糊技术应用研究
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摘要
模糊技术、神经网络与遗传算法是计算智能的三大信息科学，是智能控制领域的三个重要基础工具。如何将模糊技术、神经网络与遗传算法互相交叉，有机地结合起来，已成为众多研究者关注的焦点。本论文围绕这一热点进行了两方面的研究。
一方面，在研究了遗传算法的基本原理、理论基础以及模糊逻辑与神经网络融合的基础与途径之后，详细研究了基于联接机制的模糊神经网络构成方法。围绕这一模糊神经网络结构，首先提出了一种基于遗传算法的三阶段优化策略。在给定初始参数基础上，利用基于十进制编码的遗传算法实现模糊神经网络的结构优化，用基于二进制编码的遗传算法实现模糊神经网络的参数优化。仿真结果表明该优化策略是有效的且简单易用。
上述优化策略是利用专家经验数据来完成网络结构与参数优化的，网络的性能很大程度取决于专家经验的准确程度，为了补偿或减少人为因数的影响，针对控制系统，本论文又提出了一种基于去模糊优化的模糊神经网络控制器。在利用上述三阶段优化策略获得网络的结构和参数后，重构网络控制器的去模糊化部分，进一步细调控制变量的隶属函数，实现控制器性能的优化。从仿真结果可以看出，引入去模糊优化后，控制器的性能有所改善。
上述两种优化算法均是基于经验数据对的，当未能有效获取经验数据时，上述方法很难实现。基于最优控制的思想，—种基于遗传算法的模糊神经网络最优控制方法被提出。在测得被控对象输入输出的基础上，通过对控制系统的过程模拟，利用遗传算法优化包含控制器性能的指标来离线寻找最优的模糊神经网络控制器结构和参数。经过遗传算法训练的模糊神经网络控制器被接入模糊神经网络智能控制系统中。仿真结果表明，利用此方法实现的控制，系统的动态性能和静态性能都优于用常规模糊控制器实现的控制。
另一方面，探讨了遗传算法、神经模糊技术在中医分型诊断中应用的可行性。针对具体的中医疾病一类风湿性关节炎，提出一基于模糊神经网络的分型诊断系统。在利用基于互信息的遗传算法实现了临床症状的压缩之后，以此为基础利用
As three information sciences in computational intelligence, fuzzy Logic, neural networks and genetic algorithms are three important basic tools in intelligent control. Many research workers have concerned on the integration of techniques in these three sciences recently. This paper's work focus on the application of the integration in two ways.
On the one hand, after studying the basic principles and theories of genetic algorithm, and studying the base and way of the integration of fuzzy logic and neural networks, the structure of connectionist fuzzy neural network is studied at some length. Around this structure, an optimization strategy with three steps is presented firstly. Based on the initial parameters that have been determined in the first step, the structure of a fuzzy neural network is optimized by using a genetic algorithm with the decimal coding scheme, and the parameters are optimized with the binary coding scheme. Numerical simulations show that the optimization strategy mentioned above is available and easy to use.
Though the optimization strategy mentioned above use experiment data from expert to optimize the network structure and parameters, the performance of the network depends on the accuracy of expert experience to a great extent. In order to compensate and eliminate the influence of manmade factors, a fuzzy neural network controller based on the optimization of defuzzification is presented in this paper again. After the fuzzy neural network is got through using the strategy mentioned above, the defuzzification part of it is reconstructed, and the membership functions of the control action is further refined. Then the performance of the controller is optimized. The results of computer simulation
一方面，在研究了遗传算法的基本原理、理论基础以及模糊逻辑与神经网络融合的基础与途径之后，详细研究了基于联接机制的模糊神经网络构成方法。围绕这一模糊神经网络结构，首先提出了一种基于遗传算法的三阶段优化策略。在给定初始参数基础上，利用基于十进制编码的遗传算法实现模糊神经网络的结构优化，用基于二进制编码的遗传算法实现模糊神经网络的参数优化。仿真结果表明该优化策略是有效的且简单易用。
上述优化策略是利用专家经验数据来完成网络结构与参数优化的，网络的性能很大程度取决于专家经验的准确程度，为了补偿或减少人为因数的影响，针对控制系统，本论文又提出了一种基于去模糊优化的模糊神经网络控制器。在利用上述三阶段优化策略获得网络的结构和参数后，重构网络控制器的去模糊化部分，进一步细调控制变量的隶属函数，实现控制器性能的优化。从仿真结果可以看出，引入去模糊优化后，控制器的性能有所改善。
上述两种优化算法均是基于经验数据对的，当未能有效获取经验数据时，上述方法很难实现。基于最优控制的思想，—种基于遗传算法的模糊神经网络最优控制方法被提出。在测得被控对象输入输出的基础上，通过对控制系统的过程模拟，利用遗传算法优化包含控制器性能的指标来离线寻找最优的模糊神经网络控制器结构和参数。经过遗传算法训练的模糊神经网络控制器被接入模糊神经网络智能控制系统中。仿真结果表明，利用此方法实现的控制，系统的动态性能和静态性能都优于用常规模糊控制器实现的控制。
另一方面，探讨了遗传算法、神经模糊技术在中医分型诊断中应用的可行性。针对具体的中医疾病一类风湿性关节炎，提出一基于模糊神经网络的分型诊断系统。在利用基于互信息的遗传算法实现了临床症状的压缩之后，以此为基础利用
As three information sciences in computational intelligence, fuzzy Logic, neural networks and genetic algorithms are three important basic tools in intelligent control. Many research workers have concerned on the integration of techniques in these three sciences recently. This paper's work focus on the application of the integration in two ways.
On the one hand, after studying the basic principles and theories of genetic algorithm, and studying the base and way of the integration of fuzzy logic and neural networks, the structure of connectionist fuzzy neural network is studied at some length. Around this structure, an optimization strategy with three steps is presented firstly. Based on the initial parameters that have been determined in the first step, the structure of a fuzzy neural network is optimized by using a genetic algorithm with the decimal coding scheme, and the parameters are optimized with the binary coding scheme. Numerical simulations show that the optimization strategy mentioned above is available and easy to use.
Though the optimization strategy mentioned above use experiment data from expert to optimize the network structure and parameters, the performance of the network depends on the accuracy of expert experience to a great extent. In order to compensate and eliminate the influence of manmade factors, a fuzzy neural network controller based on the optimization of defuzzification is presented in this paper again. After the fuzzy neural network is got through using the strategy mentioned above, the defuzzification part of it is reconstructed, and the membership functions of the control action is further refined. Then the performance of the controller is optimized. The results of computer simulation
引文
1. K.L. Self. Fuzzy logic design. IEEE Spectrum. 1990, 27(4): 42-44
2．范晓英，陆培新，陈文楷．一个新型的模糊控制器．控制理论与应用．1995，12(5)：597-601
3. Y.F. Li, C.C. Lan. Development of fuzzy algorithms for servo systems. IEEE Control System Magazine. April 1989: 65-72
4. Jianmin Xu, Xiaoping Fan, Qijie Zhou et al. Adaptive fuzzy position/force control of constrained flexible-link robotic manipulators. 第二届全球华人智能控制与智能自动化大会(上卷)，中国．西安，1997：248-253
5. E. M. Scharf, N.J. Mandic. The application of a fuzzy controller to the control of a multi-degree-freedom robot arm. M. Sugeno. Industrial Application of Fuzzy Control, Amsterdam: North Holland, 1985: 41-62
6. M. Sugeno. Industrial Application of Fuzzy Control, Amsterdam: North Holland, 1985.
7. C.C. Lee. Fuzzy logic in control systems: fuzzy logic controller—parts Ⅰ & Ⅱ. IEEE Trans. Syst. Man Cybern.. 1990, SMC-20(2): 404-435
8. L.A. Zadeh. Fuzzy logic. IEEE Computer. 1988: 83-93
9. T. Sejnowski, C. Rosenberg. NETtalk: a parallel network that learns to read aloud. Technical Report JHU/EECS-86/01, EECS Department, Johns Hopkins University, 1986
10. K. Fukushima, S. Miyaka, T. Ito. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans. Syst. Man Cybern.. 1983, SMC-13 (5): 826-834
11. S. Grossberg. Cortical dynamics of three-dimensional form, color, and brightness perceptions. Perception and Psychophysic. 1987, 41(2): 87-116
12. A.G. barto, R.S. Sutton, C.W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern.. 1983, SMC-13 (5): 834-847
13. F.C. Chen. Backpropagation neural network for nonlinear self-tuning adaptive control. Proc. Of IEEE Intelligent Machines. 1898: 274-27914. D.E. Rumelhart, J.L. Mcclelland. Parallel Distributed Processing, Vol. 1-Vol. 2, MIT Press, 1986
15．李晓忠，汪培庄，罗承忠．模糊神经网络．贵州科技出版社，1994
16．赵振宁，徐用懋．模糊神经网络的基础与应用．西安科学技术出版社，1996
17．王耀南．智能腔制系统—模糊逻辑·专家系统·神经网络控制．湖南大学出版社，1996
18．刘增良，刘有才．模糊逻辑与神经网络—理论研究与探索．北京航空航天大学出版社，1996
19．张良杰，李衍达．模糊神经网络技术的新近发展．信息与控制．1995，24(1)：39-45
20. C.T. Lin. Neural fuzzy control systems with structure and parameter learning. World Scientific Publishing Co. Pte. Ltd., 1994
21．沈建强，李平．神经模糊技术的研究现状与展望．控制与决策．1996，11(5)：527-532
22．周志坚，毛宗源．模糊神经网络的交叉研究．电路与系统学报，1998，Vol．3，No．3，pp．81-85
23. D. E. Geldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Inc., 1989
24. Z. Michalewicz. Genetic Algorithms+Data structure=Evolution Programs. Springer-Verlng Berlin Heidelberg, 1992
25．孟庆春．基因算法及其应用．山东大学出版社，1995
26. I. Hideyuki, T. Fukuda, T. Shibata, et al.. Structure optimization of fuzzy neural network by genetic algorithm. Fuzzy Sets and Systems. 1995, 71: 257-264
27. J. J. Buckley, K.D. Reilly, K.V. Penmetcha. Backpropagation and Genetic Algorithms for Training Fuzzy Neural Nets. Proceedings of the Fifth IEEE international Conference on Fuzzy Systems. Fuzzy-IEEE'96: 2-6
28. SuSu Yao, Chengjian Wei, Zhenya He. Evolving Fuzzy Neural Networks for Extracting Rules. Processes of the Fifth IEEE International Conference on Fuzzy Systemmms. Fuzzy-IEEE'96: 361-367
29. L.A. Zadeh. Fuzzy sets. Information and Control. 1965, 8: 338-353
30. J.A.Goguen. L-fuzzy sets. Journal of Mathematics, Analysis and Application. 1967, 18: 145-174
31. J.A.Geguen. The logic of inexact concepts. Synthese. 1969, 19: 325-373
32. L A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Memorandum ERL-M 411, University of California, Berkeley, CA, October??1973
33. J.F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems. 1987, 24: 1-26
34. E. H. Mamdani. Applications of fuzzy set theory to control systems. Fuzzy Automta and Decision Processes, M.M.Gupta, et al., Eds., Amsterdam: North-Holland, 1977: 77-88
35. W.J.M. Kickert. Fuzzy Theories on Decisionmaking. Martinns Mijhoff, Leiden, The Netherlands, 1978
36. L.A. Zadeh. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems. 1983, 11: 199-227
37. J. Kacprzyk, R. R. Yager, Eds.. Management Decision Support Systems using Fuzzy Seta and Possibility. Verlag TUV, Rheinland, koln, Germany, 1985
38. H. J. Zimmermann. Using fuzzy sets in operations research. European Journal of Operational Research. 1983, 13: 201-216
39. J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981
40. J.C.Bezdek. Analysis of Fuzzy Information. Vol. Ⅱ Artificial Intelligence and Decision Systems, Boca Raton, 1987
41. E. Sanchez. Medical diagnosis and composite fuzzy relations, in Advanced in Fuzzy Set Theory and Applications, M. M. Gupta, et al., Amsterdam North-Holland, 1979: 437-444
42．焦李成．神经网络系统理论．西安电子科技大学出版社，1991，34-36
43. M. Sugeno. An Introductory Survey of Fuzzy Control. Inform Sci.. 1985, 36: 59-83
44．陈理君，符建豪．微机模糊控制．武汉工业大学出版社，1992
45. Li-Xin Wang. Adaptive Fuzzy Systems and Control Design and Stability Analysis, PTR Prentice-Hall., 1994
46. W.S. McCulloch, W. Pitts. A logic Calculus of The Ideal Imminent in Nervous Activity, Bulletin of ath. Biophys, 1943, 5: 115-133
47. D.D. Hebb. The Organization of Behavior. New York: John Wiley & Sons, 1949
48. R. Rosenblatt. Principles, of Neurodynamics. New York: Spartan Book, 195949. B. Widrow, M.E. Hoff. Adaptive switching circuits 1960 IRE WESCON Cony. Record. Part 4, 1960, 8: 96-104
50. S.C. Lee, E.T. Lee. Fuzzy sets and neural networks. J. Cybernetics. 1974, 4: 83-103
51. S.C. Lee, E.T. Lee. Fuzzy neural networks. Math. wsci.. 1975, 23: 151-177
52. B. Kosko. Neural networks and fuzzy systems. Prentice-Hall, Englewood, Cliffs, 1992
53. M. E. Cohen, D.L. Hudson. Approaches to the handing of fuzzy input DATA in neural networks. IEEE Fuzzy'92, 1992: 93-100
54. P.J. Werbos. Neurocontrol and Elastic Fuzzy Logic: Capabilities, Concepts, and Applications. IEEE Trans. on Industrial Electronics, 1993, 40(2): 170-180
55. C.T. Lin, C. S.G. Lee. Neural-Network-based Fuzzy Logic Control and Decision System. IEEE Trans. Comput, 1989: 1320-1336
56. J. M. Keller, et al. Neural Network Implementation of Fuzzy Logic. Fuzzy Sets and Systems. 1992, 45 (1): 1-12
57. P. Arabshahi, et al. Fuzzy Control of Backpropagation. IEEE Fuzzy'92, 1992: 967-972
58. J.J. Choi, et al. Fuzzy parameter adaptation in neural systems. Proc. Internal J. Conf. Neural Networks, Baltimore (1992), Vol. Ⅰ, 232-238
59．张良杰，李衍达．基于模糊逻辑与组合插值技术的新型一维全局优化算法．清华大学学报，人工智能与模式识别专辑，1994(Sup4)
60．黄苏南，邵惠鹤．一种智能控制器．自动化学报，1997，23(1)：116-119
61. H. Tagaki, N. Suzuki, T. Koda et al. Neural networks designed on approximate reasoning architecture and their applications. IEEE Trans. on Neural Network, 1992, 3 (5): 752-760
62. H. C. Chen, I. H. Fang. Reinforcement learning of a neural network using a fuzzy logic controller: Application to seismic topographic data. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996: 1166-1170
63．孟庆春．遗传算法及其应用．山东大学出版社，1995：3
64．徐滇生，毛绪谨．遗传算法在自控领域的应用初探．中国自动化学会控制理论及应用年会，1992，10．709-712
65．宋庆伟，寇纪凇，李敏强等．基于遗传算法的非线形系统参数辩识方法．第二届全球华人智能控制与智能自动化大会(上卷)，中国．西安，1997：122-12566．方建安，邵世煌．遗传算法应用研究．中国自动化学会第八届全国模式识别与人工智能会议，成都，1993，7．549-554
67．方建安，邵世煌．采用遗传算法学习的神经网络控制器．控制与决策，1993，8(3)：208-212
68. J.J. Grefenstette. Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. on System Man and Cybernetics, 1986, SMC-16(1): 122-128
69. K. Shimojima, T. Fukuda, Hasegawa. Self-tuning fuzzy modeling with adaptive membership function, rules and hierarchical structure based on genetic algorithm. Fuzzy Sets and Systems, 1995, 71: 295-309
70. S. K. Pal, D. Bhandari. Genetic algorithms with fuzzy fitness function for object extraction using cellular networks. Fuzzy Sets and Systems, 1994, 65: 129-139
71. J.J. Buckley, Y. Hayashi. Fuzzy genetic algorithm and applications. Fuzzy Sets and System, 1994, 61: 129-136
72．窦永丰，张国会，何军红．遗传算法和模糊控制相结合的方法及其应用．第二届全球华人智能控制与智能自动化大会(中卷)，中国．西安，1997：1848-1854
73. Xin Yao. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems. 1993, 8: 539-567
74. D. Whitley, T. Hanson. Optimizing neural networks using faster, more accurate genetic search. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer (Ed.), Morgan Kaufmann. San Mateo, CA, 1989: 391-396
75. T.P. Caudell, C.P. Dolan. Parametric connectivity: Training of constrained networks using genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer(Ed.), Morgan Kaufmann. San Mateo, CA, 1989: 370-374
76. D. Whitley, T. Starkweather, C. Bogart. Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing. 1990, 14: 347-361
77. D. Montana, L. Davis. Training feedforward neural networks using genetic algorithms. Proceedings of Eleven International Joint Conference on Artificial Intelligence. Morgan Kaufmann. San Mateo, CA. 1989: 762-767
78. D.B. Fogel, L.J. Fogel, V.W. Porto. Evolving neural networks. Biologic cybernetic.??1990, 63: 487-493
79. P. Bartlett, T. Downs. Training a neural network with a genetic algorithm. Technical Report. Dept. Of Elec. Eng.. Univ. Of Queensland. January 1990
80. J.D. Schaffer, R. A. Caruana, L.J. Eshelman. Using genetic search to exploit the emergent behavior of neural networks. Physica D. 1990, 42: 244-248
81. D. Whitley, C. Bogart. The evolution of connectivity: Pruning neural networks using genetic algorithms. Proceedings of International Joint Conference on Neural Networks, I-134-I-137, Eribaum, Hillsdale, NJ, 1990
82. B. Maricic, Z. Nikolov. GENNET-system for computer aided neural network design using genetic algorithms. Proceedings of International Joint Conference on Neural Networks, I-102-I-105, Eribaum, Hillsdale, NJ, 1990
83. D.G. Stork, S. Walker, M.Burns, B. Jackson Preadaption in neural circuits. Proceedings of International Joint Conference on Neural Networks, I-202-I-205, Eribaum, Hillsdale, NJ, 1990
84. R.K. Belew, J. McInerney, N.N. Schraudolph. Evolving Networks: Using Genetic Algorithm with Connectionist Learning. Technical Report#CS90-174(Revised), Computer Science & Eng. Dept(C-014), Univ. Of California at San Diego, La Jolla, CA. Februray 1991
85. S.A. Harp, T. Samad, A. Guha. Towards the genetic synthesis of neural networks. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer(Ed.), Morgan Kaufmann. San Mateo, CA, 1989: 360-369
86．周志坚，毛宗源．一种基于遗传算法的模糊神经网络结构和参数优化．华南理工大学学报(自然科学版)，1999，27(1)：26-32
87. M. Russo. FuGeNeSys—A Fuzzy Genetic Neural System for Fuzzy Modeling. IEEE Trans. On Fuzzy Systems. 1998, 6(3): 373-388
88. U.D. Hanebeck, G.K. Schmidt, Genetic optimization of fuzzy networks. Fuzzy Sets and Systems. 1996, 79(1): 59-68
89. J. Kim, Y. Moon, B.P. Zeigler. Designing Fuzzy Net Controllers Using Genetic Algorithms. IEEE Control Systems Magazine, 1995, 15 (3): 66-72
90. J.H. Holland. Adaptation in Natural and Artificial Systems, 1st ed., 1975, 2nd??ed., Cambridge, MA: MIT press, 1992
91．恽为民，席裕庚．遗传算法的运行机理分析．控制理论与应用．1996，13(3)：297-304
92．恽为民，席裕庚．遗传算法的全局收敛性和计算效率分析．控制理论与应用．1996，13(4)：455-460
93．段玉倩，贺家李．遗传算法及其改进．电力系统及其自动化学报．1998，10(1)：39-51
94．葛红．采用遗传算法自学习的基于神经网络结构的自组织的模糊控制器．华南理工大学硕士学位论文．1997，3：27
95．席裕庚，柴天佑，恽为民．遗传算法综述．控制理论与应用．1996，13(6)：697-708
96. K.A. DeJong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Dissertation, University Microfilms, No. 76-9381, University of Michigan, Ann Arber, 1975
97. A. Brindle. Genetic Algorithms for Function Optimization, Doctoral Dissertation, Univ. Of Alberta, 1981
98．恽为民．基于遗传的机器人运动规划．上海交通大学博士论文，1995
99. L.B. Booker, D.E. Geldberg, J.H. Holland. Classifier Systems and Genetic Algorithms. Artificial Intelligence, 1989, 40: 235-282
100. T. Back. The Interaction of Mutation Rate, Selection and Self-Adaptation within a Genetic Algorithm. In Parallel Problem Solving from Nature, 2, Amsterdsm: North Holland, 1992: 84-94
101. G. Syswerda. Uniform Crossover in Genetic Algorithms. 3rd Int. Conf. On genetic Algorithms, 1989: 2-9
102. Whitley, et al. Genitor Ⅰ: A Distributed Genetic Algorithm. J. Expt. Ther. Intell., 1990, 2: 189-214
103. J. J. Grefenstette, R. Gepal, B. Rosmaita, D. VaaGucht. Genetic Algorithms for the Traveling Salesman Problem. Proc. of Intern. Conf. on Genetic Algorithms and Their Application. J.J. Grefenstette, Ed. Lawrence Earlbaum, 1985: 160-168
104. J. D. Bagley. The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms. Dissertation Abstracts International, 1967, 28 (12)
105. Z. Michalewicz, et al. Genetic Algorithms and Optimal Control Problems. Proc. 29th. IEEE conf. Decision and Control. 1990: 1664-1666106. D.E. Goldberg. Real-coded Genetic Algorithm, Virtual Alphabets and Blocking. Complex Systems, 1991, 5: 139-167
107. D.J. Cavicchio. Reproductive Adaptive Plans. Proc. of the ACM 1972 Annual Conf., 1972: 1-11
108. W.M. Spears, K.A. DeJong. An Analysis of Multi-Point Crossover. Foundations of Genetic Algorithms, 1991: 301-315
109. D.E. Goldberg, R. Lingle. Alleles, Loci, and the Traveling Salesman Problem. Proc. of Intern. Conf. on genetic Algorithms and Their Applications, 1985: 154-159
110. L. Davis. Job Shop Scheduling with Genetic Algorithms. Proceedings of International Conference on Genetic Algorithms and Their Applications, 1985: 136-140
111. D. Smith. Bin Packing with Adaptive Search. Proceedings of International Conference on Genetic Algorithms and Their Applications, 1985: 202-206
112. Z. Michalewicz, et al. A modified Genetic Algorithm for Optimal Control Problems. Computers Math. Applic. 1992, 23(12): 83-94
113. J. Bosworth, N. Foo, B.P. Zeigler. Comparison of Genetic Algorithms with Conjugate Gradient Methods. CR2093, NASA
114. R.S. Rosenberg. A Computer Simulation of a Biological Population. Unpublished Manuscript, 1966
115. D.R. Frantz. Non-linearities in Genetic Adaptive Search. Doctoral Dissertations Abstracts Int.. 1970, 31(9)
116. Y. Davidor. Genetic Algorithms and Robotics. Singapore World Scientific Publishing, 1991
117. D.E. Goldberg, et al.. Messy genetic Algorithms: Motivation, Analysis and First Results. Complex Syst., 1989, 3: 493-530
118. J.D. Schaffer, et al. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. Proc. 3rd Conf. Genetic Algorithms, 1989: 51-60
119. L. Davis, Ed. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991
120. L. Davis. Adapting Operator Probabilities in Genetic Algorithms. Proc. 3rd Genetic??Algorithm. 1989: 61-69
121. T. C. Fogarty. Varying the Probability of Mutation in Genetic Algorithms. Proc. 3rd Conf. Genetic Algorithms. 1989: 104-109
122．陈国良，王煦法，庄镇泉，等．遗传算法及其应用．第1版．人民邮电出版社，1996：28-41
123．汪培庄．模糊集合论及其应用．上海科学技术出版社，1983，229-230
124. M. Srinivas, L.M. Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systams, Man and Cybernetics, 1994, 24 (4): 656-667
125．王士同．神经模糊系统及其应用．第1版．北京航空航天大学出版社，1998：2-3
126．王隆杰，毛宗源．利用神经网络进行推理的模糊控制器．控制理论与应用，1994，11(4)：508-512
127. S.K. Halgamuge, M. Glesner. Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets and Systems, 1994, 65: 1-12
128．杨煜普，许晓鸣，张钟俊．基于模糊神经网络的控制规则获取及置信度估计问题．模式识别与人工智能，1994，7(1)：53-59
129. S. Horikawa, T. Furuhashi, and Y. Uchikawa. On Fuzzy Modeling using Fuzzy Neural Networks with the Back-propagation Algorithm. IEEE Transaction on Neural Networks, 1992, 3 (5): 801-806
130．应行仁．采用BP神经网络记忆模糊规则的控制．自动化学报，1991，17(1)：63-67
131．廖俊，林建亚．基于神经网络的自适应模糊控制器．信息与控制，1995，24(5)：312-315
132．王耀南，宋明，童调生．一种基于神经网络的自组织模糊控制与应用．模式识别与人工智能，1994，7(4)：285-291
133. A. Athalye, D. Edwards, V.S. Manoranjan, A. De Sam Lazaro. On designing a fuzzy control system using an optimization algorithm. Fuzzy Sets and Systems. 1993, 56: 281-290
134．李士勇．糊控制·神经网络控制和智能控制论．哈尔滨：哈尔滨工业大学出版社，1996
135．秦笃烈，鲍亦万．中医计算机模拟及专家系统概论．人民卫生出版社，1989：258-305
136．季守贤，胡方．类风湿性关节炎的中医证候分析，长春中医学院学报，Vol．11(No．49)，1995：33
137．史秉璋，苏炳华，郑彼得等．实用医学统计手册．福建科学技术出版社，1987：319-358138．方积乾，徐勇勇，余松林等．医学统计学与电脑实验．上海科学技术出版社
139．孙益鑫．论模糊数学与中医学．中国医药学报，1996，11(1)：15-19
140．江一平，刘明芝，姜灿文．模糊数学在中医四虚证鉴别诊断中的初步应用．中医药学报．1984，(5)：13-17
141．李昂．模糊数学与颈椎病的分型诊断．中国中医骨伤科杂志．199，3(6)：22-24
142．余乃登，刘晴．脑血管病辅助诊断系统．数理医药学杂志．1995，8(1)：65-66
143．朱育风，丰国炳，彭德和等．应用多元隶属函数对中药禹粮石进行品质分类．数理医药学杂志．1995，8(4)：306-309
144．张定—．中医辨证的几个模糊数学模型．数理医药学杂志．1994，7(1)：10-11
145．张衍芳．模糊数学在中医辨证中的应用．中医研究．1992，5(1)：5-7
146．赵尚华，潘政．血栓性静脉炎中医电脑诊疗程序的模糊数学模型．山西中医药．1988，4(2)：33-35
147．舒柏华，叶维新，张永学，安锐．应用模糊数学建立甲状腺疾病鉴别诊断系统的研究．同济医科大学学报．1995，24(4)：311315
148．杨以桡，龙启铭，杨云志等．模糊学方法在小儿心血管疾病微机专家系统中的应用．生物医学工程学杂志．1995，12(4)：346-349
149．陈五零，王存冉，郭荣江．神经元网络模型及其在中医诊断方面的应用．中医医药学杂志．1991，71(2)：111-113
150．周礼杲．人工神经网络在生物医学中的应用．国外医学生物医学工程分册．1991，14(2)：63-68
151．宋红，林家瑞．用于医学辅助诊断的神经网络方法的应用研究．生物医学工程学杂志．1996，13(2)：141-144
152．秦明新，曹彤，鲍洪涛等．评价心脏功能的人工神经网络方法．中国生物医学工程学报．1995，14(2)：176-177
153．蔡煜东，朱海鸾．流行性脑脊髓炎预报的人工神经网络模型．生物医学工程学杂志．1994，11(4)：314-318
154．林维鉴．BP网络用于中医痹证证候分类．福建中医学院学报．1997，7(4)：41-43
155．韦哲，程自峰，张世范．人工神经网络方法在功能性食管病诊断分类研究中的应用．中国医疗器械杂志．1997，21(3)：154-157
156．李志良，曾鸽鸣，胡芳等．神经网络在肝硬化病因鉴别诊断中的应用．中国生物医学工程学??报．1997，16(1)：92-93
157．吴新根，吕维雪．基于神经网络的临床症状资料压缩和选择．生物医学工程学杂志．1996，13(4)：317-319
158．吕安林，唐一鹏，赵树民，晶．疾病信息熵定量分析辅助诊断的研究．数理医药学杂志．1998，11(1)：11-13
159．郭业才．模糊熵在慢性阻塞性肺病鉴别诊断中的应用．中国卫生统计．1995，12(2)：14-17
160. Wentian Li. Mutual Information Functions versus Correlation Functions. Journal of Statistical Physics. 1990,60(5/6) :823-837
161．孟庆生．信息论．西安交通大学出版社，1987：10-19
162．王奇，谭芬来，梁伟雄等．中医证候量化的临床流行病学研究初探．广州中医学院学报．1992．9(4)：224-228
2．范晓英，陆培新，陈文楷．一个新型的模糊控制器．控制理论与应用．1995，12(5)：597-601
3. Y.F. Li, C.C. Lan. Development of fuzzy algorithms for servo systems. IEEE Control System Magazine. April 1989: 65-72
4. Jianmin Xu, Xiaoping Fan, Qijie Zhou et al. Adaptive fuzzy position/force control of constrained flexible-link robotic manipulators. 第二届全球华人智能控制与智能自动化大会(上卷)，中国．西安，1997：248-253
5. E. M. Scharf, N.J. Mandic. The application of a fuzzy controller to the control of a multi-degree-freedom robot arm. M. Sugeno. Industrial Application of Fuzzy Control, Amsterdam: North Holland, 1985: 41-62
6. M. Sugeno. Industrial Application of Fuzzy Control, Amsterdam: North Holland, 1985.
7. C.C. Lee. Fuzzy logic in control systems: fuzzy logic controller—parts Ⅰ & Ⅱ. IEEE Trans. Syst. Man Cybern.. 1990, SMC-20(2): 404-435
8. L.A. Zadeh. Fuzzy logic. IEEE Computer. 1988: 83-93
9. T. Sejnowski, C. Rosenberg. NETtalk: a parallel network that learns to read aloud. Technical Report JHU/EECS-86/01, EECS Department, Johns Hopkins University, 1986
10. K. Fukushima, S. Miyaka, T. Ito. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans. Syst. Man Cybern.. 1983, SMC-13 (5): 826-834
11. S. Grossberg. Cortical dynamics of three-dimensional form, color, and brightness perceptions. Perception and Psychophysic. 1987, 41(2): 87-116
12. A.G. barto, R.S. Sutton, C.W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern.. 1983, SMC-13 (5): 834-847
13. F.C. Chen. Backpropagation neural network for nonlinear self-tuning adaptive control. Proc. Of IEEE Intelligent Machines. 1898: 274-27914. D.E. Rumelhart, J.L. Mcclelland. Parallel Distributed Processing, Vol. 1-Vol. 2, MIT Press, 1986
15．李晓忠，汪培庄，罗承忠．模糊神经网络．贵州科技出版社，1994
16．赵振宁，徐用懋．模糊神经网络的基础与应用．西安科学技术出版社，1996
17．王耀南．智能腔制系统—模糊逻辑·专家系统·神经网络控制．湖南大学出版社，1996
18．刘增良，刘有才．模糊逻辑与神经网络—理论研究与探索．北京航空航天大学出版社，1996
19．张良杰，李衍达．模糊神经网络技术的新近发展．信息与控制．1995，24(1)：39-45
20. C.T. Lin. Neural fuzzy control systems with structure and parameter learning. World Scientific Publishing Co. Pte. Ltd., 1994
21．沈建强，李平．神经模糊技术的研究现状与展望．控制与决策．1996，11(5)：527-532
22．周志坚，毛宗源．模糊神经网络的交叉研究．电路与系统学报，1998，Vol．3，No．3，pp．81-85
23. D. E. Geldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Inc., 1989
24. Z. Michalewicz. Genetic Algorithms+Data structure=Evolution Programs. Springer-Verlng Berlin Heidelberg, 1992
25．孟庆春．基因算法及其应用．山东大学出版社，1995
26. I. Hideyuki, T. Fukuda, T. Shibata, et al.. Structure optimization of fuzzy neural network by genetic algorithm. Fuzzy Sets and Systems. 1995, 71: 257-264
27. J. J. Buckley, K.D. Reilly, K.V. Penmetcha. Backpropagation and Genetic Algorithms for Training Fuzzy Neural Nets. Proceedings of the Fifth IEEE international Conference on Fuzzy Systems. Fuzzy-IEEE'96: 2-6
28. SuSu Yao, Chengjian Wei, Zhenya He. Evolving Fuzzy Neural Networks for Extracting Rules. Processes of the Fifth IEEE International Conference on Fuzzy Systemmms. Fuzzy-IEEE'96: 361-367
29. L.A. Zadeh. Fuzzy sets. Information and Control. 1965, 8: 338-353
30. J.A.Goguen. L-fuzzy sets. Journal of Mathematics, Analysis and Application. 1967, 18: 145-174
31. J.A.Geguen. The logic of inexact concepts. Synthese. 1969, 19: 325-373
32. L A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Memorandum ERL-M 411, University of California, Berkeley, CA, October??1973
33. J.F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems. 1987, 24: 1-26
34. E. H. Mamdani. Applications of fuzzy set theory to control systems. Fuzzy Automta and Decision Processes, M.M.Gupta, et al., Eds., Amsterdam: North-Holland, 1977: 77-88
35. W.J.M. Kickert. Fuzzy Theories on Decisionmaking. Martinns Mijhoff, Leiden, The Netherlands, 1978
36. L.A. Zadeh. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems. 1983, 11: 199-227
37. J. Kacprzyk, R. R. Yager, Eds.. Management Decision Support Systems using Fuzzy Seta and Possibility. Verlag TUV, Rheinland, koln, Germany, 1985
38. H. J. Zimmermann. Using fuzzy sets in operations research. European Journal of Operational Research. 1983, 13: 201-216
39. J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981
40. J.C.Bezdek. Analysis of Fuzzy Information. Vol. Ⅱ Artificial Intelligence and Decision Systems, Boca Raton, 1987
41. E. Sanchez. Medical diagnosis and composite fuzzy relations, in Advanced in Fuzzy Set Theory and Applications, M. M. Gupta, et al., Amsterdam North-Holland, 1979: 437-444
42．焦李成．神经网络系统理论．西安电子科技大学出版社，1991，34-36
43. M. Sugeno. An Introductory Survey of Fuzzy Control. Inform Sci.. 1985, 36: 59-83
44．陈理君，符建豪．微机模糊控制．武汉工业大学出版社，1992
45. Li-Xin Wang. Adaptive Fuzzy Systems and Control Design and Stability Analysis, PTR Prentice-Hall., 1994
46. W.S. McCulloch, W. Pitts. A logic Calculus of The Ideal Imminent in Nervous Activity, Bulletin of ath. Biophys, 1943, 5: 115-133
47. D.D. Hebb. The Organization of Behavior. New York: John Wiley & Sons, 1949
48. R. Rosenblatt. Principles, of Neurodynamics. New York: Spartan Book, 195949. B. Widrow, M.E. Hoff. Adaptive switching circuits 1960 IRE WESCON Cony. Record. Part 4, 1960, 8: 96-104
50. S.C. Lee, E.T. Lee. Fuzzy sets and neural networks. J. Cybernetics. 1974, 4: 83-103
51. S.C. Lee, E.T. Lee. Fuzzy neural networks. Math. wsci.. 1975, 23: 151-177
52. B. Kosko. Neural networks and fuzzy systems. Prentice-Hall, Englewood, Cliffs, 1992
53. M. E. Cohen, D.L. Hudson. Approaches to the handing of fuzzy input DATA in neural networks. IEEE Fuzzy'92, 1992: 93-100
54. P.J. Werbos. Neurocontrol and Elastic Fuzzy Logic: Capabilities, Concepts, and Applications. IEEE Trans. on Industrial Electronics, 1993, 40(2): 170-180
55. C.T. Lin, C. S.G. Lee. Neural-Network-based Fuzzy Logic Control and Decision System. IEEE Trans. Comput, 1989: 1320-1336
56. J. M. Keller, et al. Neural Network Implementation of Fuzzy Logic. Fuzzy Sets and Systems. 1992, 45 (1): 1-12
57. P. Arabshahi, et al. Fuzzy Control of Backpropagation. IEEE Fuzzy'92, 1992: 967-972
58. J.J. Choi, et al. Fuzzy parameter adaptation in neural systems. Proc. Internal J. Conf. Neural Networks, Baltimore (1992), Vol. Ⅰ, 232-238
59．张良杰，李衍达．基于模糊逻辑与组合插值技术的新型一维全局优化算法．清华大学学报，人工智能与模式识别专辑，1994(Sup4)
60．黄苏南，邵惠鹤．一种智能控制器．自动化学报，1997，23(1)：116-119
61. H. Tagaki, N. Suzuki, T. Koda et al. Neural networks designed on approximate reasoning architecture and their applications. IEEE Trans. on Neural Network, 1992, 3 (5): 752-760
62. H. C. Chen, I. H. Fang. Reinforcement learning of a neural network using a fuzzy logic controller: Application to seismic topographic data. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996: 1166-1170
63．孟庆春．遗传算法及其应用．山东大学出版社，1995：3
64．徐滇生，毛绪谨．遗传算法在自控领域的应用初探．中国自动化学会控制理论及应用年会，1992，10．709-712
65．宋庆伟，寇纪凇，李敏强等．基于遗传算法的非线形系统参数辩识方法．第二届全球华人智能控制与智能自动化大会(上卷)，中国．西安，1997：122-12566．方建安，邵世煌．遗传算法应用研究．中国自动化学会第八届全国模式识别与人工智能会议，成都，1993，7．549-554
67．方建安，邵世煌．采用遗传算法学习的神经网络控制器．控制与决策，1993，8(3)：208-212
68. J.J. Grefenstette. Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. on System Man and Cybernetics, 1986, SMC-16(1): 122-128
69. K. Shimojima, T. Fukuda, Hasegawa. Self-tuning fuzzy modeling with adaptive membership function, rules and hierarchical structure based on genetic algorithm. Fuzzy Sets and Systems, 1995, 71: 295-309
70. S. K. Pal, D. Bhandari. Genetic algorithms with fuzzy fitness function for object extraction using cellular networks. Fuzzy Sets and Systems, 1994, 65: 129-139
71. J.J. Buckley, Y. Hayashi. Fuzzy genetic algorithm and applications. Fuzzy Sets and System, 1994, 61: 129-136
72．窦永丰，张国会，何军红．遗传算法和模糊控制相结合的方法及其应用．第二届全球华人智能控制与智能自动化大会(中卷)，中国．西安，1997：1848-1854
73. Xin Yao. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems. 1993, 8: 539-567
74. D. Whitley, T. Hanson. Optimizing neural networks using faster, more accurate genetic search. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer (Ed.), Morgan Kaufmann. San Mateo, CA, 1989: 391-396
75. T.P. Caudell, C.P. Dolan. Parametric connectivity: Training of constrained networks using genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer(Ed.), Morgan Kaufmann. San Mateo, CA, 1989: 370-374
76. D. Whitley, T. Starkweather, C. Bogart. Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing. 1990, 14: 347-361
77. D. Montana, L. Davis. Training feedforward neural networks using genetic algorithms. Proceedings of Eleven International Joint Conference on Artificial Intelligence. Morgan Kaufmann. San Mateo, CA. 1989: 762-767
78. D.B. Fogel, L.J. Fogel, V.W. Porto. Evolving neural networks. Biologic cybernetic.??1990, 63: 487-493
79. P. Bartlett, T. Downs. Training a neural network with a genetic algorithm. Technical Report. Dept. Of Elec. Eng.. Univ. Of Queensland. January 1990
80. J.D. Schaffer, R. A. Caruana, L.J. Eshelman. Using genetic search to exploit the emergent behavior of neural networks. Physica D. 1990, 42: 244-248
81. D. Whitley, C. Bogart. The evolution of connectivity: Pruning neural networks using genetic algorithms. Proceedings of International Joint Conference on Neural Networks, I-134-I-137, Eribaum, Hillsdale, NJ, 1990
82. B. Maricic, Z. Nikolov. GENNET-system for computer aided neural network design using genetic algorithms. Proceedings of International Joint Conference on Neural Networks, I-102-I-105, Eribaum, Hillsdale, NJ, 1990
83. D.G. Stork, S. Walker, M.Burns, B. Jackson Preadaption in neural circuits. Proceedings of International Joint Conference on Neural Networks, I-202-I-205, Eribaum, Hillsdale, NJ, 1990
84. R.K. Belew, J. McInerney, N.N. Schraudolph. Evolving Networks: Using Genetic Algorithm with Connectionist Learning. Technical Report#CS90-174(Revised), Computer Science & Eng. Dept(C-014), Univ. Of California at San Diego, La Jolla, CA. Februray 1991
85. S.A. Harp, T. Samad, A. Guha. Towards the genetic synthesis of neural networks. Proceedings of the Third International Conference on Genetic Algorithms and Their Applications, J.D. Schaffer(Ed.), Morgan Kaufmann. San Mateo, CA, 1989: 360-369
86．周志坚，毛宗源．一种基于遗传算法的模糊神经网络结构和参数优化．华南理工大学学报(自然科学版)，1999，27(1)：26-32
87. M. Russo. FuGeNeSys—A Fuzzy Genetic Neural System for Fuzzy Modeling. IEEE Trans. On Fuzzy Systems. 1998, 6(3): 373-388
88. U.D. Hanebeck, G.K. Schmidt, Genetic optimization of fuzzy networks. Fuzzy Sets and Systems. 1996, 79(1): 59-68
89. J. Kim, Y. Moon, B.P. Zeigler. Designing Fuzzy Net Controllers Using Genetic Algorithms. IEEE Control Systems Magazine, 1995, 15 (3): 66-72
90. J.H. Holland. Adaptation in Natural and Artificial Systems, 1st ed., 1975, 2nd??ed., Cambridge, MA: MIT press, 1992
91．恽为民，席裕庚．遗传算法的运行机理分析．控制理论与应用．1996，13(3)：297-304
92．恽为民，席裕庚．遗传算法的全局收敛性和计算效率分析．控制理论与应用．1996，13(4)：455-460
93．段玉倩，贺家李．遗传算法及其改进．电力系统及其自动化学报．1998，10(1)：39-51
94．葛红．采用遗传算法自学习的基于神经网络结构的自组织的模糊控制器．华南理工大学硕士学位论文．1997，3：27
95．席裕庚，柴天佑，恽为民．遗传算法综述．控制理论与应用．1996，13(6)：697-708
96. K.A. DeJong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. Dissertation, University Microfilms, No. 76-9381, University of Michigan, Ann Arber, 1975
97. A. Brindle. Genetic Algorithms for Function Optimization, Doctoral Dissertation, Univ. Of Alberta, 1981
98．恽为民．基于遗传的机器人运动规划．上海交通大学博士论文，1995
99. L.B. Booker, D.E. Geldberg, J.H. Holland. Classifier Systems and Genetic Algorithms. Artificial Intelligence, 1989, 40: 235-282
100. T. Back. The Interaction of Mutation Rate, Selection and Self-Adaptation within a Genetic Algorithm. In Parallel Problem Solving from Nature, 2, Amsterdsm: North Holland, 1992: 84-94
101. G. Syswerda. Uniform Crossover in Genetic Algorithms. 3rd Int. Conf. On genetic Algorithms, 1989: 2-9
102. Whitley, et al. Genitor Ⅰ: A Distributed Genetic Algorithm. J. Expt. Ther. Intell., 1990, 2: 189-214
103. J. J. Grefenstette, R. Gepal, B. Rosmaita, D. VaaGucht. Genetic Algorithms for the Traveling Salesman Problem. Proc. of Intern. Conf. on Genetic Algorithms and Their Application. J.J. Grefenstette, Ed. Lawrence Earlbaum, 1985: 160-168
104. J. D. Bagley. The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms. Dissertation Abstracts International, 1967, 28 (12)
105. Z. Michalewicz, et al. Genetic Algorithms and Optimal Control Problems. Proc. 29th. IEEE conf. Decision and Control. 1990: 1664-1666106. D.E. Goldberg. Real-coded Genetic Algorithm, Virtual Alphabets and Blocking. Complex Systems, 1991, 5: 139-167
107. D.J. Cavicchio. Reproductive Adaptive Plans. Proc. of the ACM 1972 Annual Conf., 1972: 1-11
108. W.M. Spears, K.A. DeJong. An Analysis of Multi-Point Crossover. Foundations of Genetic Algorithms, 1991: 301-315
109. D.E. Goldberg, R. Lingle. Alleles, Loci, and the Traveling Salesman Problem. Proc. of Intern. Conf. on genetic Algorithms and Their Applications, 1985: 154-159
110. L. Davis. Job Shop Scheduling with Genetic Algorithms. Proceedings of International Conference on Genetic Algorithms and Their Applications, 1985: 136-140
111. D. Smith. Bin Packing with Adaptive Search. Proceedings of International Conference on Genetic Algorithms and Their Applications, 1985: 202-206
112. Z. Michalewicz, et al. A modified Genetic Algorithm for Optimal Control Problems. Computers Math. Applic. 1992, 23(12): 83-94
113. J. Bosworth, N. Foo, B.P. Zeigler. Comparison of Genetic Algorithms with Conjugate Gradient Methods. CR2093, NASA
114. R.S. Rosenberg. A Computer Simulation of a Biological Population. Unpublished Manuscript, 1966
115. D.R. Frantz. Non-linearities in Genetic Adaptive Search. Doctoral Dissertations Abstracts Int.. 1970, 31(9)
116. Y. Davidor. Genetic Algorithms and Robotics. Singapore World Scientific Publishing, 1991
117. D.E. Goldberg, et al.. Messy genetic Algorithms: Motivation, Analysis and First Results. Complex Syst., 1989, 3: 493-530
118. J.D. Schaffer, et al. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. Proc. 3rd Conf. Genetic Algorithms, 1989: 51-60
119. L. Davis, Ed. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991
120. L. Davis. Adapting Operator Probabilities in Genetic Algorithms. Proc. 3rd Genetic??Algorithm. 1989: 61-69
121. T. C. Fogarty. Varying the Probability of Mutation in Genetic Algorithms. Proc. 3rd Conf. Genetic Algorithms. 1989: 104-109
122．陈国良，王煦法，庄镇泉，等．遗传算法及其应用．第1版．人民邮电出版社，1996：28-41
123．汪培庄．模糊集合论及其应用．上海科学技术出版社，1983，229-230
124. M. Srinivas, L.M. Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systams, Man and Cybernetics, 1994, 24 (4): 656-667
125．王士同．神经模糊系统及其应用．第1版．北京航空航天大学出版社，1998：2-3
126．王隆杰，毛宗源．利用神经网络进行推理的模糊控制器．控制理论与应用，1994，11(4)：508-512
127. S.K. Halgamuge, M. Glesner. Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets and Systems, 1994, 65: 1-12
128．杨煜普，许晓鸣，张钟俊．基于模糊神经网络的控制规则获取及置信度估计问题．模式识别与人工智能，1994，7(1)：53-59
129. S. Horikawa, T. Furuhashi, and Y. Uchikawa. On Fuzzy Modeling using Fuzzy Neural Networks with the Back-propagation Algorithm. IEEE Transaction on Neural Networks, 1992, 3 (5): 801-806
130．应行仁．采用BP神经网络记忆模糊规则的控制．自动化学报，1991，17(1)：63-67
131．廖俊，林建亚．基于神经网络的自适应模糊控制器．信息与控制，1995，24(5)：312-315
132．王耀南，宋明，童调生．一种基于神经网络的自组织模糊控制与应用．模式识别与人工智能，1994，7(4)：285-291
133. A. Athalye, D. Edwards, V.S. Manoranjan, A. De Sam Lazaro. On designing a fuzzy control system using an optimization algorithm. Fuzzy Sets and Systems. 1993, 56: 281-290
134．李士勇．糊控制·神经网络控制和智能控制论．哈尔滨：哈尔滨工业大学出版社，1996
135．秦笃烈，鲍亦万．中医计算机模拟及专家系统概论．人民卫生出版社，1989：258-305
136．季守贤，胡方．类风湿性关节炎的中医证候分析，长春中医学院学报，Vol．11(No．49)，1995：33
137．史秉璋，苏炳华，郑彼得等．实用医学统计手册．福建科学技术出版社，1987：319-358138．方积乾，徐勇勇，余松林等．医学统计学与电脑实验．上海科学技术出版社
139．孙益鑫．论模糊数学与中医学．中国医药学报，1996，11(1)：15-19
140．江一平，刘明芝，姜灿文．模糊数学在中医四虚证鉴别诊断中的初步应用．中医药学报．1984，(5)：13-17
141．李昂．模糊数学与颈椎病的分型诊断．中国中医骨伤科杂志．199，3(6)：22-24
142．余乃登，刘晴．脑血管病辅助诊断系统．数理医药学杂志．1995，8(1)：65-66
143．朱育风，丰国炳，彭德和等．应用多元隶属函数对中药禹粮石进行品质分类．数理医药学杂志．1995，8(4)：306-309
144．张定—．中医辨证的几个模糊数学模型．数理医药学杂志．1994，7(1)：10-11
145．张衍芳．模糊数学在中医辨证中的应用．中医研究．1992，5(1)：5-7
146．赵尚华，潘政．血栓性静脉炎中医电脑诊疗程序的模糊数学模型．山西中医药．1988，4(2)：33-35
147．舒柏华，叶维新，张永学，安锐．应用模糊数学建立甲状腺疾病鉴别诊断系统的研究．同济医科大学学报．1995，24(4)：311315
148．杨以桡，龙启铭，杨云志等．模糊学方法在小儿心血管疾病微机专家系统中的应用．生物医学工程学杂志．1995，12(4)：346-349
149．陈五零，王存冉，郭荣江．神经元网络模型及其在中医诊断方面的应用．中医医药学杂志．1991，71(2)：111-113
150．周礼杲．人工神经网络在生物医学中的应用．国外医学生物医学工程分册．1991，14(2)：63-68
151．宋红，林家瑞．用于医学辅助诊断的神经网络方法的应用研究．生物医学工程学杂志．1996，13(2)：141-144
152．秦明新，曹彤，鲍洪涛等．评价心脏功能的人工神经网络方法．中国生物医学工程学报．1995，14(2)：176-177
153．蔡煜东，朱海鸾．流行性脑脊髓炎预报的人工神经网络模型．生物医学工程学杂志．1994，11(4)：314-318
154．林维鉴．BP网络用于中医痹证证候分类．福建中医学院学报．1997，7(4)：41-43
155．韦哲，程自峰，张世范．人工神经网络方法在功能性食管病诊断分类研究中的应用．中国医疗器械杂志．1997，21(3)：154-157
156．李志良，曾鸽鸣，胡芳等．神经网络在肝硬化病因鉴别诊断中的应用．中国生物医学工程学??报．1997，16(1)：92-93
157．吴新根，吕维雪．基于神经网络的临床症状资料压缩和选择．生物医学工程学杂志．1996，13(4)：317-319
158．吕安林，唐一鹏，赵树民，晶．疾病信息熵定量分析辅助诊断的研究．数理医药学杂志．1998，11(1)：11-13
159．郭业才．模糊熵在慢性阻塞性肺病鉴别诊断中的应用．中国卫生统计．1995，12(2)：14-17
160. Wentian Li. Mutual Information Functions versus Correlation Functions. Journal of Statistical Physics. 1990,60(5/6) :823-837
161．孟庆生．信息论．西安交通大学出版社，1987：10-19
162．王奇，谭芬来，梁伟雄等．中医证候量化的临床流行病学研究初探．广州中医学院学报．1992．9(4)：224-228