先进控制技术的集成及应用研究
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摘要
迄今为止，已在工业上获得成功应用的先进控制策略主要包括：自适应控制、变结构控制、预测控制、鲁棒控制、模糊控制、专家控制、神经网络控制等，各种控制策略各有利弊。因此多种控制策略之间相互渗透、交叉和结合的复合控制策略，是先进控制技术的发展方向。这些复合控制策略克服了单一控制策略的不足，取长补短、具有更优良的性能，能更好地满足不同应用场合的不同要求，因而获得了更广泛的应用。
所谓先进控制技术的集成包括两个方面：一方面是将几种先进的智能控制方法融合在一起，构成具有高度自主能力的智能混合控制系统；另一方面是将智能控制与最优控制相结合，构成智能复合控制，以便取长补短，优势互补，提高整体控制水平。因此智能混合控制和智能复合控制在非线性多变量控制系统中的应用已经成为当前控制领域的一个研究热点，并已成为解决各类复杂系统控制问题的重要工具，在理论上和实际应用中都有十分重要的意义。
本文在掌握国内外研究现状的基础上，在这一领域展开了进一步的研究工作，主要研究内容概括如下：
(1)描述了模糊系统与神经网络的等价性和互换性：从映射角度来看，模糊系统和神经网络都具有非线性函数的逼近能力；模糊系统与神经网络具有等价性和互换性：模糊系统可以用一等价的神经网络来表示，同样神经网络也可以用一等价的模糊系统来表示；其次，描述了模糊系统和神经网络融合的五种形态，并提出一种结构等价的模糊神经网络，给出了它们的实现方法。
(2)研究了智能混合控制。并以改进的遗传算法在模糊神经网络参数学习中的应用问题为例加以说明。首先提出了两种改进的遗传算法：一种是加入BP算子的遗传算法，这不仅利用了GA与BP各自的优点，而且极大的提高了参数的搜索效率；另一种是改变遗传算子的遗传算法，并证明了这种改进的遗传算法的性能明显优于标准的遗传算法。然后将改进的遗传算法应用于模糊神经网络参数学习中，通过构造模糊神经网络控制器进行仿真，验证了方法的有效性和适用性。改进的遗传算法在学习过程中表现出很强的全局优化能力，为复杂非线性及组合优化问题提供了一个很好的解
So far, the advanced control technology strategy has obtained successful application in the industry and mainly includes: adaptive control, variable structure controls, predictive control, robust control, fuzzy control, expert control, neural network control and so on. Each kind of control strategy has its special skill respectively and has certain problems in certain aspects. Therefore, between each kind of control strategy mutually seeping, overlapping and the union. Moreover the new compound control strategy is continuously emerging along with the science and technology development. These compound control strategies overcome the insufficiency of single control strategy, making up for one's deficiency by learning from others' strong points, and have a finer performance. These strategies can even better satisfy the different application situation's request and obtain a more widespread application.
Integration of advanced control includes two aspects. One is the fusion of several advanced intelligent control methods. It constructs advanced hybrid intelligent control system, which has highly independent capacity. The other is the integration of intelligent control technology and traditional control theory. It constructs intelligent compound control system, and has the advantage of improving the whole control level. So the application of advanced hybrid intelligent control and intelligent compound control in nonlinear multivariable control system has become a hotspot in current control field and an important tool for resolving all kinds of complex system control problems. It has very important meaning in theory and actual application.
This paper is on the basis of the domestic and foreign research status, further the research work in this domain. And the main contents are as follows.
(1) To describe equivalence and interchangeability of the fuzzy system and neural network : Firstly, the fuzzy system and the neural network all have approximation ability of
所谓先进控制技术的集成包括两个方面：一方面是将几种先进的智能控制方法融合在一起，构成具有高度自主能力的智能混合控制系统；另一方面是将智能控制与最优控制相结合，构成智能复合控制，以便取长补短，优势互补，提高整体控制水平。因此智能混合控制和智能复合控制在非线性多变量控制系统中的应用已经成为当前控制领域的一个研究热点，并已成为解决各类复杂系统控制问题的重要工具，在理论上和实际应用中都有十分重要的意义。
本文在掌握国内外研究现状的基础上，在这一领域展开了进一步的研究工作，主要研究内容概括如下：
(1)描述了模糊系统与神经网络的等价性和互换性：从映射角度来看，模糊系统和神经网络都具有非线性函数的逼近能力；模糊系统与神经网络具有等价性和互换性：模糊系统可以用一等价的神经网络来表示，同样神经网络也可以用一等价的模糊系统来表示；其次，描述了模糊系统和神经网络融合的五种形态，并提出一种结构等价的模糊神经网络，给出了它们的实现方法。
(2)研究了智能混合控制。并以改进的遗传算法在模糊神经网络参数学习中的应用问题为例加以说明。首先提出了两种改进的遗传算法：一种是加入BP算子的遗传算法，这不仅利用了GA与BP各自的优点，而且极大的提高了参数的搜索效率；另一种是改变遗传算子的遗传算法，并证明了这种改进的遗传算法的性能明显优于标准的遗传算法。然后将改进的遗传算法应用于模糊神经网络参数学习中，通过构造模糊神经网络控制器进行仿真，验证了方法的有效性和适用性。改进的遗传算法在学习过程中表现出很强的全局优化能力，为复杂非线性及组合优化问题提供了一个很好的解
So far, the advanced control technology strategy has obtained successful application in the industry and mainly includes: adaptive control, variable structure controls, predictive control, robust control, fuzzy control, expert control, neural network control and so on. Each kind of control strategy has its special skill respectively and has certain problems in certain aspects. Therefore, between each kind of control strategy mutually seeping, overlapping and the union. Moreover the new compound control strategy is continuously emerging along with the science and technology development. These compound control strategies overcome the insufficiency of single control strategy, making up for one's deficiency by learning from others' strong points, and have a finer performance. These strategies can even better satisfy the different application situation's request and obtain a more widespread application.
Integration of advanced control includes two aspects. One is the fusion of several advanced intelligent control methods. It constructs advanced hybrid intelligent control system, which has highly independent capacity. The other is the integration of intelligent control technology and traditional control theory. It constructs intelligent compound control system, and has the advantage of improving the whole control level. So the application of advanced hybrid intelligent control and intelligent compound control in nonlinear multivariable control system has become a hotspot in current control field and an important tool for resolving all kinds of complex system control problems. It has very important meaning in theory and actual application.
This paper is on the basis of the domestic and foreign research status, further the research work in this domain. And the main contents are as follows.
(1) To describe equivalence and interchangeability of the fuzzy system and neural network : Firstly, the fuzzy system and the neural network all have approximation ability of
引文
1 Austrom, K. J., Directions in Intelligent Control, Plenary Session[J], IFAC International Symposium, ITAC91 (Intelligent Tuning and Adaptive Control), 1991, 15-17
2. 张智星等.神经-模糊和软计算[M],西安:西安交通大学出版社,2002
3. McCulloch, W.S., Pitts, W.A. A Logic Calculus of the Idea Imminent in Nervous Activity [J], Bulletin of Mathematical Biophysics, 1943, 5, 115-133
4. Hebb, D.O., the Organization of Behavior[M], New York, John Wiley & Son, 1949, 1-9
5. Rosenblatt, F., Principles of Neurodynamics[M], New York, Spartan Book, 1959, 1-20
6. Hopfield, J.J., Neural network and Physical Systems with Emergent Collective Computational Abilities[J], Proceeding of National Academy of Science, 1982, 79
7. Hopfield, J.J. Neurons with Graded Response Have Collective Computational Properties Link Those of Two-state Neurons[J]. Ibid, 1984, 81(10): 3088-3092
8. Hinton, G.E., Sejnowski, T.J., Boltzman Machines: Constraint Satisfaction Networks that learn[R], Carnegie-Mellon University, Technology Report, 1984
9. Rumelhart, D.E., McClelland, J.L., Parallel Distributed Processing[M], MIT Press, 1986
10. Hecht-Neilsen, Counter Propagation Networks[J], Proc of IEEE first International Conference on Neural Networks, 1987, 2, 19-32
11. Zadch, L.A. Fuzzy sets. Information and Control[J], 1965, 8, 338-353
12. Zadeh, L.A. A Rationale for Fuzzy Control, Journal of Dynamic Systems[J]. Measurement, and Control, 1972, G94, 3-4
13. Mamdani, E.H. Application of Fuzzy Algorithms for Control of Simple Dynamic Plant[J]. IEEE Proceedings, 1974, 121(12): 1585-1588
14. King, P.J., Mamdani, E.H. The application of Fuzzy Control System to an Industrial Processes[J], Automatica. 1977, 13(3): 235-242
15. Ostergarad, J.J., Fuzzy Logic Control of a Heat Exchange Process[A], In Fuzzy Automat and Decision Processes[C]. M. M. Gupta, G. N. Saridis and B. R. Gaines Eds, Amsterdam. North-Holland, 1977, 285-320
16. Pappis, C.P. A Fuzzy Logic Controller for a Traffic Junction[J], IEEE Trans SMC, 1977,??7(10): 707-717
17. Procyk, T.J., Mamdani, E.H. A linguistic self-organizing process controller[J], Automatica, 1979, 15(1): 15-30
18. Tomohiro Takagi, Mochio Sugeno. Fuzzy Identification of Systems and Its Applications to Modeling and control[J], IEEE Trans. SMC, 1985, 15(1): 116-13
19. Lee, S. C, Lee, E. T. Fuzzy sets and neural networks[J], Journal of Cybernetics, 1974, 4,83-103
20. Takagi, T., Hayashi, I.. Artificial-neural-network-driven fuzzy reasoning[A], Int. Workshop on Fuzzy System Applications[C], 1988, 217-218
21. Sugeno, M., An introductory survey of fuzzy controlfJ], Information Science, 1985, 1, 59-83
22. Hayashi, I., Hiroyoshi, N., Wakami, N., Neural-network-driven fuzzy reasoning model[A], Proceedings of the 28th SICE Annual Conference[C], Volume2 (of 2) 1989
23. Hayashi, Y., Buckley, J.J., Czognala, E. Systems engineering applications for fuzzy neural networks[J], A Systems Engineering, 1992, 2, 232-236
24. Hayashi, Y., Buckley, J. J., Czognala, E., Direct fuzzification of neural network and fuzzified delta rule[A], Proc.2nd Int. Conf. On Fuzzy Logic and Neural Networks[C], Iizuka, Japan, 1992,73-76
25. Hayashi, Y., Buckley, J. J., Czognala, E., Systems engineering application for fuzzy neural networks[A], Pro. Internat. J. Conf. Neural Network[C], Baltimore, 1992, 2, 412-418
26. Kosko, B., Neural networks and fuzzy systems[A], Prentice Hall International Inc[C], Singapore, 1992
27. Proceedings of the 1996 Artificial Neural Networks in Engineering[A], ANNIE96. Intelligent Engineering Systems through Artificial Neural Networks Proceedings of the 1996 Artificial Neural Networks in Engineering[C], ANNIE'96 Nov 10-13 1996 v61996 St. louis Controller. AICHEJ, 1989, 35(11): 1835-1844
28. Proceeding of the 1997 Artificial Neural Networks in Engineering Conference[C], ANNIE'97,Nov 9-12,1997,v7
29. Proceedings of the 1998 Artificial Networks in Engineering Conference[C], ANNIE, Nov 1-4, 1998, v830. Asim, R., Raymond, M., Fuzzy logic, neural networks and brain-like learning[A]. Proceedings of the 1997 IEEE International Conference on Neural Networks. Part 1(of 4)[C], 1997, 1, 9-12
31. Jang, Jyh-Shing Roger, ANFIS: Adaptive-Network-Based Fuzzy Inference System[J], IEEE Transactions on Systems, Man, and Cybernetics, 23, 1993,665-684
32. Nürnberger, A., Nauck, D., Kruse, R. Neuro-fuzzy control based on the NEFCON model: recent developments[J]. Soft Computing, 1999, 2(4): 168-182
33. Tran Haoi L., Osowski S. Neuro-fuzzy TSK network for approximation of static and dynamic functions[J]. Control and Cybernetics, 2002, 31(2):309-326
34. Nauck, D. and Kruse, R. Neuro-fuzzy systems for function approximation[J]. Fuzzy Sets and Systems, 1999, 10(1):261-271
35．陈非，敬忠良，姚晓东．一种模糊神经网络的快速参数学习算法．控制理论与应用[J]，2002，19(4)：583-587
36．彭志平．一种基于NFCS形态的模糊神经网络的学习算法[J]．计算机研究与发展，2002，39(11)：1436-1441
37. Sanchez, E., Genetic algorithm, neural networks and fuzzy logic systems[A]. In Proc. Second International Conference on Fuzzy Logic and Neural Networks[C], Iizuka. 1992, 17-19
38. Machado, R. J. and Rocha, A. F., Evolutive fuzzy neural networks[A]. In Proc. First IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'92)[C], San Diego, 1992, 493-500
39. Schaffer, J. D., Combinations of genetic algorithms and neural networks or fuzzy systems[A]. In Zurada J. M., Marks Ⅱ R.J., and Robinson C.J.(eds) Computational Intelligence Imitating Life[C], IEEE Press, New York. 1994, 371-382
40. Krishnamraju, P.V., Buckley. J.J., Reilly, K.D. and Hayashi, Y., Genetic learning algorithms for fuzzy neural nets[A], In Proc. Third IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'94)[C], Orlando, 1994, 1969-1974
41. Nobre, F.S.M., Genetic-neural-fuzzy systems: A promising fusion[A]. In Proc. Fourth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'95)[C], Yokohama, 1995, 259-265
42. Hanebeck, U., and Schmidt, G. K. Genetic optimization of fuzzy networks[J]. Fuzzy??Sets and Systems, 1996, 79, 59-68
43. Zhang, L.Q, Kandel, A., Genetic-guided compensatory neuro fuzzy systems[A], In Proc. Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU'96)[C], Granada, 1996, 181-185
44. Chisci L, et al. Indirect and Implicit Adaptive Control of the Benchmark Plant[J]. Automatica, 1994, 30(4): 577～584
45. Mossca E. Adaptive Predictive Control with Meansquare Input Constraints[J], Automatica, 1992, 28(3): 593～597
46. Datta A. Adaptive Internal Model Control: Design and Stability Analysis[J]. Automatica, 1996, 32(2): 261～266
47. Lee J Y. Receding Horizon Predictive Control for the Continuoustime Systems[A]. Proc 32th SICE Annual Conf (International Session)[C], 1993, 1067～1072
48. Kothare M V, et al. Robust Constrained Model Predictive Control Using Linear Matrix Inequalities[J]. Automatica, 1996, 32(10): 1361～1379
49. Papa Vasilou G C, et al. A Descent Algorithm for a Min-Max Problem in Model Predictive Control[A]. Proc 30th Conference on Decision and Control[C], Brighton, 1991, 1869～1870
50. Rossiter J A., Feasibility and Stability Results for Constrained Stable Generalized Predictive Control[J]. Automatica, 1995, 31 (6): 863～877
51. Zheng A, et al. Robust Stability of Constrained Model Predicitive Control[A]. Proc American Control Conference[C], 1993, 379～383
52. Allwright J C. On Min-max Model Predictive Control, Advances in Model Based Predictive Control[M]. Oxford University Press, 1994
53. Lee J. H, et al. Worst-Case Formulations of Model Predictive Control for Systems with Bounded Parameters[J]. Automatica, 1997, 33(5): 763～781
54. Allwright, J.C, Papavasiliou, G..C. On Linear Programming and Robust Model Predictive Control Using Impulse Response[J]. System and Control Letters. 1992, 18(3): 159～164
55．王永骥，徐桂英，涂健．水轮发电机组L~∞范数性能指标预测控制[J]．控制理论与应用，1991，8(4)：386～392
56. Tsypkin Y Z. Robust stochastic Control Using the Internal Model Principle and Internal??Model Control[J]. Int J Control, 1995, 61(4): 809～822
57. Vandeursen J M, et al. Internal Model Control with Improved Distarbance Rejection[J]. Int J Control, 1995, 62(4): 983～999
58. Gawthrop P J, et al. Emulator-based Control and Internal Model Control: Compelmentary Approaches to Robust Control Design[J]. Antomatica, 1996, 32(8): 1223～1227
59．张化光等．模糊广义预测控制及其应用[J]．自动化学报，1993，19(1)：9～17
60．李静如，李尚春．模糊预测控制及其应用研究．控制理论与应用[J]，1992，9(3)：283～286
61. Van Can H J L, et al. Neural models in Predictive Control[A]. Int. Conf. Computer Applications in Biotechnology[C]. Garmisch Germany, 1995, 95～100
62．冯思波等．基于神经网络技术的解耦及容错解耦控制策略[J]．信息与控制，1992，21(6)：363～368
63. Wang L X, et al. Generating Fuzzy Rules by Learning Form Example[J]. IEEE Trans on SMC, 1992, 22(6): 1414～1427
64. Wang L X, Design and Analysis of Fuzzy Identifiers of Nonlinear Dynamic Systems[J]. IEEE Trans on Auto Cont, 1995, 40(1): 11～23
65. Rechalet J. Model Predictive Heuristic Control: Applications to Industrial Processes[J]. Automatica, 1978, 17(5): 413～428
66. Cutler C R, Ramaker B L. Dynamic Matrix Control—A Computer Control Algorithm[A]. Proc JACC Sam Francisco[C], 1980
67. Rouhani R, Mehra R K. Model Algorithmic Control Basic Theoretical Properties[J]. Automatica, 1982, 18(4): 401～414
68. Garcia G E, Morshedi A M. Quadratic Programming Solution of Dynamic Matrix Control(QDMC)[J]. Chem Eng Commun, 1984, 46(1): 73～87
69. Grosclidier P. The IDCOM-M Control[A]. IFAC Workshop on Model-Based Control[C]. Atlanta GA, 1988
70. Clarke D W. Generalized Predictive Control—Part 1 and Part 2 Basic Algorithm[J]. Automatica, 1987, 23(2): 137～162
71. Lelic M A, Zarrop M B. Generalized Pole Placement Self-tuning Controller. Int J Control, 1987, 46(2): 547～56872. Ydstie B E. Extended Horizon Adaptive Control[A]. Budapest: Proe IFAC 9th World Congress[C], 1984, 2:911～915
73. De Keyser R M C. A Self-tuning Multi-Step. Predictor Application[J], Automatica, 1981, 117(1): 167～174
74. Garcia C E, Morari M. Internal Model Control, Ind, Engng Chem, Process Des. Dev., 1982, 21(2): 308～332,472～494
75. Brosilow C B. Joseph B. Inferential Control of Processes[J]. AICHE J, 1978, 24(3): 485～509
76. Kwon W H, Byun D G. Receding Horizon Tracking Control as a Predictive Control and its Stability Properties[J]. Int J Control, 1989, 50(5): 1807～1824
77．金元郁，顾兴源．改进广义预测控制算法[J]，信息与控制，1990，19(3)：8～13
78．金元郁，顾兴源．改进的多变量广义预测控制算法[J]．信息与控制，1990，19(6)：20～23
79．顾兴源，毛志忠．一种基于广义预测控制的极点配置白适应控制算法[J]．控制与决策，1992，7(3)：221～224
80. Cloud D J, Kouvaritakis B. Characteristic Decomposition and Multivariable Generalisation of Prelictive Self-tuning Control[J]. Proc IEE, 1988, 135(3): 165～181
81．吕剑虹，徐治皋，陈来九．特征结构下多变量预测控制系统的闭环反馈结构及其应用[J]．控制理论与应用，1992，9(2)：174～182
82. Pedro A. On Generalized Predictive Control: Two Alternative Formulations. Automatica, 1989, 25(5): 753～755
83. Visser H R. Unified Predictive Pole-placemend Controller: an Integrated Approach[J]. Control and Computer, 1989, 17(3): 71～74
84. Peng L. Tuning Generalized Predictive Control Using a Pole-placement Criterion[A]. Proc. American Control Conference[C]. 1992,2391～2395
85．孙茂相，王曙光，吴学曼，封锡盛，关玉林．广义预测极点配置前馈控制的一种算法[J]．信息与控制，1992，21(6)：338～342
86．徐立鸿，冯纯伯．加权多步预测控制[J]．自动化学报，1991，17(6)：658～668
87．徐立鸿，冯纯伯．多变量加权多步预测控制[J]．控制理论与应用，1992，9(3)：238～244
88．徐立鸿，冯纯伯．递推自适应加权多步预测控制[J]．自动化学报，1992，18(5)：??580～584
89．周德云．一种新的鲁棒多变量广义预测控制算法[J]．控制与决策，1992，7(6)：420～426
90. Irving E, et al. Adaptive Generalized Predictive Control with Multiple Reference Model. Lund[J]; 2th, IF AC Workshop on Adaptive systems in Control and Signal Processing, 1986
91. Gorez R. On a Generalized Predictive Control Algorithm[J]. System & Control Letters, 1987, (9): 369～377
92. Koh E K. Generalized Pole-placement for Systems Requiring Fast Sumpling[A]. Proc American Control Conference[C], 1992, 713～714
93. Chai T. Decoupling Design of Multivariable Generalized Predictive Control[J]. IEEE Proceedings, 141(3): 197～201
94．诸静，黄抗美．复杂系统的广义预测模糊控制．电工技术学报[J]，1997，12(2)：7～12
95. Kershenbaum L S, et al. An Extended Horizon Feedback/Feed for Ward Self-Tuning Controller[J]. AICHEJ, 1989, 35(11): 1835～1844
96. Linkens D A, Mahfoub M. Generalized Predictive Control with Feed for Ward (GPCF) for Multivaraible Anaesthesia[J], Int. Control, 1992, 56(5): 1039～1057
97. Song J J, Park S W. Neural Model Predictive Control for Nonlinear Chemical Processes[J], J Chemical Engineering of Japan, 1993
98．邹志云．新型自适应模型算法控制算式[J]．控制与决策，1988，3：11～16
99．古钟壁．具有误差预测修正的预测控制算法[J]．控制与决策，1992，7(6)：432～436
100．王伟．一种广义预测自适应的直接算法[J]．自动化学报，1996，22(3)：270～277
101．周德云，陈新海，佟明安．广义预测鲁棒自适应控制器[J]．控制与决策，1991，6(4)：265～270
102. Eishabei A, Dumont, G., Elnaggar, A. Stability and Convergence Analyses of an Adaptive GPC Based on State-Space Model. Int J Control[J], 1995, 61(1): 193～210
103. Yoon T, Clarke, D.W. Adaptive Predictive Control of the Benchmark Plant[J]. Automatica, 1994, 30(4): 621～628
104. De Nicolao G. An Adaptive Predictive Regulator with Input Saturations[J]. Automatica, 1996, 32(4): 597～601
105. Chisci L. Indirect and Implicit Adaptive Control of the Benchmark Plant[J].??An-tomatic, 1994, 30(4): 577～584
106. Mossca E. Adaptive Predictive Control with Meansquare Input Constraints[J], Automatica, 1992, 28(3): 593～597
107. Datta A. Adaptive Internal Model Control: Design and Stability Analysis[J]. Automatica, 1996, 32(2): 261～266
108. Lee J Y. Receding Horizon Predictive Control for the Continuoustime Systems[A]. Proc 32th SICE Annual Conf (International Session)[C], 1993, 1067～1072
109. Kothare M V. Robust Constrained Model Predictive Control Using Linear Matrix Inequalities[J]. Automatica, 1996, 32(10): 1361～1379
110. Papa Vasilou G C. A Descent Algorithm for a Min-Max Problem in Model Predictive Control[A]. Proc 30th Conference on Decision and Control[C], Brighton, 1991, 1869～1870
111. Rossiter J A. Feasibility and Stability Results for Constrained Stable Generalized Predictive Control. Automatica[J], 1995, 31 (6): 863～877
112. Zheng A. Robust Stability of Constrained Model Predicitive Control[A]. Proc American Control Conference[C], 1993,379～383
113. Allwright J C. On Min-max Model Predictive Control, Advances in Model Based Predictive Control[M]. Oxford University Press, 1994
114. Lee J. H. Worst-Case Formulations of Model Predictive Control for Systems with Bounded Parameters[J]. Automatica, 1997, 33(5): 763～781
115. Allwright J C. On Linear Programming and Robust Model Predictive Control Using Impulse Response[J]. System and Control Letters. 1992, 18(3): 159～164
116. Tsypkin Y Z. Robust stochastic Control Using the Internal Model Principle and Internal Model Control[J]. Int J Control, 1995, 61(4): 809～822
117. Vandeursen J M. Internal Model Control with Improved Distarbance Rejection[J]. Int J Control, 1995, 62(4): 983～999
118. Gawthrop P J. Emulator-based Control and Internal Model Control: Compelmentary Approaches to Robust Control Design[J], Antomatica, 1996, 32(8): 1223～1227
119．张化光．模糊广义预测控制及其应用[J]．自动化学报，1993，19(1)：9～17
120．李静如，李尚春．模糊预测控制及其应用研究．控制理论与应用[J]，1992，9(3)：283～286121. Van Can H J L. Neural models in Predictive Control. Int. Conf. Computer Applications in Biotechnology[J], Garmisch Germany, 1995, 95～100
122．冯思波．基于神经网络技术的解耦及容错解耦控制策略[J]，信息与控制，1992，21(6)：363～368
123．张向东，孙薇，金锦良．多层前馈模糊神经网络进行图像识别[J]，计算机科学与应用，2001，18(5)：1-4
124．王玉涛，颜华，周建常，王师．一种自组织模糊神经元网络控制学习方法及其应用[J]，东北大学学报，1998，19(2)：191-194
125．古钟璧，王祯学，胡丽莉．模糊神经网络自适应控制[J]，四川大学学报，1998，35(1)：71-76
126．廖俊，朱世强，林建亚．一种新型的模糊神经网络控制器[J]，仪器仪表学报，1998，19(2)：163-167
127. Chin-Teng Lin, Ya-Ching Lu. A neural fuzzy system with fuzzy supervised learning[J], IEEE Transaction on System, Man, and Cybernetics-Part B: Cybernetics, 1996, 26(5): 744-763
128．周明，孙树栋，遗传算法的原理与应用[M]，1999年，国防工业出版社，北京，11-13
129. WANG, Y.Q., XU, L., YU, X.L., WANG, J.H., GU, S.S. Survey on Modern Optimization Techniques. International Journal of Information and Systems Sciences
130．徐容，徐用懋，兰鸿森．多层前向网络的研究——遗传BP算法和结构优化策略[J]，自动化学报，1997，23(1)：43-49
131．穆文全，廖晓峰，虞厥邦．基于遗传算法和BP算法的多层感知机杂交训练算法[J]，电子科学学刊，1997，19(2)：190-19
132. Davis, L. Adapting Operator Probabilities in Genetic Algorithms[A], In: Proceedings of the third international conference on Genetic algorithms, Morgan Kaufmann Publishers Inc., San Francisco[C], CA, USA, 1989, 61-69
133. Kenneth Alan De Jong. An analysis of the behavior of a class of genetic adaptive systems[D], Univ. Michigan, Ann Arbor, MI, 1975
134. Whitley, D., Starkweather, T. Genitor Ⅱ: A Distributed Genetic Algorithm[J], J. Expt. Ther. Intell., 1990, 2, 189-214
135. Wang Chi-Hsu, Liu Han-Leih, and Lin Chin-Teng. Dynamic Optimal Learning Rates of a Certain Class of Fuzzy Neural Networks and its Applications with Genetic??Algorithm[J], IEEE Transaction on System, Man, and Cybernetics-Part B: Cybernetics, 2001, 31(3):467-475
136. Frank H. F., Leung, H. K. Lam, S. H. Ling and Peter K.S. Tam. Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm[J], IEEE Transactions on Neural Network, 2003, 14(1): 79-88
137. Hanaki, Y., Hashiyama, T. and Okuma, S. Accelerated evolutionary computation using fitness estimation[A], in Proc. IEEE Int. Conf. Syst., Man, Cybern.[C], 1999, 1, 643-648
138. Kenneth Alan De Jong. An analysis of the behavior of a class of genetic adaptive systems[J], Ph.D. dissertation. Univ. Michigan, Ann Arbor, MI, 1975
139. Wang L X. Generating Fuzzy Rules by Learning Form Example[J], IEEE Trans on SMC, 1992, 22(6): 1414～1427
140. Wang L X. Design and Analysis of Fuzzy Identifiers of Nonlinear Dynamic Systems[J], IEEE Trans on Auto Cont, 1995, 40(1): 11～23
141．翁学义，赵光宙．非线性预测控制方法的研究[D]，杭州：浙江大学，1998
142. Song J J, Park S W. Neural Model Predictive Control for Nonlinear Chemical Processes[J], J Chemical Engineering of Japan, 1993
143. Takagi T, Sugeno M. Fuzzy Identification of Systems and its Applications to Modeling and Control[J], IEEE Trans. SMC, 1985 (15): 116～132
144. Wang L X. Design and Analysis of Fuzzy Identifiers of Nonlinear Dynamic Sys tems[J]. IEEE Trans on Auto Cont, 1995, 40(1): 11～23
145．赵振宇，徐用懋．模糊理论和神经网络的基础与应用[M]，北京：清华大学出版社，1996，156～174
2. 张智星等.神经-模糊和软计算[M],西安:西安交通大学出版社,2002
3. McCulloch, W.S., Pitts, W.A. A Logic Calculus of the Idea Imminent in Nervous Activity [J], Bulletin of Mathematical Biophysics, 1943, 5, 115-133
4. Hebb, D.O., the Organization of Behavior[M], New York, John Wiley & Son, 1949, 1-9
5. Rosenblatt, F., Principles of Neurodynamics[M], New York, Spartan Book, 1959, 1-20
6. Hopfield, J.J., Neural network and Physical Systems with Emergent Collective Computational Abilities[J], Proceeding of National Academy of Science, 1982, 79
7. Hopfield, J.J. Neurons with Graded Response Have Collective Computational Properties Link Those of Two-state Neurons[J]. Ibid, 1984, 81(10): 3088-3092
8. Hinton, G.E., Sejnowski, T.J., Boltzman Machines: Constraint Satisfaction Networks that learn[R], Carnegie-Mellon University, Technology Report, 1984
9. Rumelhart, D.E., McClelland, J.L., Parallel Distributed Processing[M], MIT Press, 1986
10. Hecht-Neilsen, Counter Propagation Networks[J], Proc of IEEE first International Conference on Neural Networks, 1987, 2, 19-32
11. Zadch, L.A. Fuzzy sets. Information and Control[J], 1965, 8, 338-353
12. Zadeh, L.A. A Rationale for Fuzzy Control, Journal of Dynamic Systems[J]. Measurement, and Control, 1972, G94, 3-4
13. Mamdani, E.H. Application of Fuzzy Algorithms for Control of Simple Dynamic Plant[J]. IEEE Proceedings, 1974, 121(12): 1585-1588
14. King, P.J., Mamdani, E.H. The application of Fuzzy Control System to an Industrial Processes[J], Automatica. 1977, 13(3): 235-242
15. Ostergarad, J.J., Fuzzy Logic Control of a Heat Exchange Process[A], In Fuzzy Automat and Decision Processes[C]. M. M. Gupta, G. N. Saridis and B. R. Gaines Eds, Amsterdam. North-Holland, 1977, 285-320
16. Pappis, C.P. A Fuzzy Logic Controller for a Traffic Junction[J], IEEE Trans SMC, 1977,??7(10): 707-717
17. Procyk, T.J., Mamdani, E.H. A linguistic self-organizing process controller[J], Automatica, 1979, 15(1): 15-30
18. Tomohiro Takagi, Mochio Sugeno. Fuzzy Identification of Systems and Its Applications to Modeling and control[J], IEEE Trans. SMC, 1985, 15(1): 116-13
19. Lee, S. C, Lee, E. T. Fuzzy sets and neural networks[J], Journal of Cybernetics, 1974, 4,83-103
20. Takagi, T., Hayashi, I.. Artificial-neural-network-driven fuzzy reasoning[A], Int. Workshop on Fuzzy System Applications[C], 1988, 217-218
21. Sugeno, M., An introductory survey of fuzzy controlfJ], Information Science, 1985, 1, 59-83
22. Hayashi, I., Hiroyoshi, N., Wakami, N., Neural-network-driven fuzzy reasoning model[A], Proceedings of the 28th SICE Annual Conference[C], Volume2 (of 2) 1989
23. Hayashi, Y., Buckley, J.J., Czognala, E. Systems engineering applications for fuzzy neural networks[J], A Systems Engineering, 1992, 2, 232-236
24. Hayashi, Y., Buckley, J. J., Czognala, E., Direct fuzzification of neural network and fuzzified delta rule[A], Proc.2nd Int. Conf. On Fuzzy Logic and Neural Networks[C], Iizuka, Japan, 1992,73-76
25. Hayashi, Y., Buckley, J. J., Czognala, E., Systems engineering application for fuzzy neural networks[A], Pro. Internat. J. Conf. Neural Network[C], Baltimore, 1992, 2, 412-418
26. Kosko, B., Neural networks and fuzzy systems[A], Prentice Hall International Inc[C], Singapore, 1992
27. Proceedings of the 1996 Artificial Neural Networks in Engineering[A], ANNIE96. Intelligent Engineering Systems through Artificial Neural Networks Proceedings of the 1996 Artificial Neural Networks in Engineering[C], ANNIE'96 Nov 10-13 1996 v61996 St. louis Controller. AICHEJ, 1989, 35(11): 1835-1844
28. Proceeding of the 1997 Artificial Neural Networks in Engineering Conference[C], ANNIE'97,Nov 9-12,1997,v7
29. Proceedings of the 1998 Artificial Networks in Engineering Conference[C], ANNIE, Nov 1-4, 1998, v830. Asim, R., Raymond, M., Fuzzy logic, neural networks and brain-like learning[A]. Proceedings of the 1997 IEEE International Conference on Neural Networks. Part 1(of 4)[C], 1997, 1, 9-12
31. Jang, Jyh-Shing Roger, ANFIS: Adaptive-Network-Based Fuzzy Inference System[J], IEEE Transactions on Systems, Man, and Cybernetics, 23, 1993,665-684
32. Nürnberger, A., Nauck, D., Kruse, R. Neuro-fuzzy control based on the NEFCON model: recent developments[J]. Soft Computing, 1999, 2(4): 168-182
33. Tran Haoi L., Osowski S. Neuro-fuzzy TSK network for approximation of static and dynamic functions[J]. Control and Cybernetics, 2002, 31(2):309-326
34. Nauck, D. and Kruse, R. Neuro-fuzzy systems for function approximation[J]. Fuzzy Sets and Systems, 1999, 10(1):261-271
35．陈非，敬忠良，姚晓东．一种模糊神经网络的快速参数学习算法．控制理论与应用[J]，2002，19(4)：583-587
36．彭志平．一种基于NFCS形态的模糊神经网络的学习算法[J]．计算机研究与发展，2002，39(11)：1436-1441
37. Sanchez, E., Genetic algorithm, neural networks and fuzzy logic systems[A]. In Proc. Second International Conference on Fuzzy Logic and Neural Networks[C], Iizuka. 1992, 17-19
38. Machado, R. J. and Rocha, A. F., Evolutive fuzzy neural networks[A]. In Proc. First IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'92)[C], San Diego, 1992, 493-500
39. Schaffer, J. D., Combinations of genetic algorithms and neural networks or fuzzy systems[A]. In Zurada J. M., Marks Ⅱ R.J., and Robinson C.J.(eds) Computational Intelligence Imitating Life[C], IEEE Press, New York. 1994, 371-382
40. Krishnamraju, P.V., Buckley. J.J., Reilly, K.D. and Hayashi, Y., Genetic learning algorithms for fuzzy neural nets[A], In Proc. Third IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'94)[C], Orlando, 1994, 1969-1974
41. Nobre, F.S.M., Genetic-neural-fuzzy systems: A promising fusion[A]. In Proc. Fourth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'95)[C], Yokohama, 1995, 259-265
42. Hanebeck, U., and Schmidt, G. K. Genetic optimization of fuzzy networks[J]. Fuzzy??Sets and Systems, 1996, 79, 59-68
43. Zhang, L.Q, Kandel, A., Genetic-guided compensatory neuro fuzzy systems[A], In Proc. Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU'96)[C], Granada, 1996, 181-185
44. Chisci L, et al. Indirect and Implicit Adaptive Control of the Benchmark Plant[J]. Automatica, 1994, 30(4): 577～584
45. Mossca E. Adaptive Predictive Control with Meansquare Input Constraints[J], Automatica, 1992, 28(3): 593～597
46. Datta A. Adaptive Internal Model Control: Design and Stability Analysis[J]. Automatica, 1996, 32(2): 261～266
47. Lee J Y. Receding Horizon Predictive Control for the Continuoustime Systems[A]. Proc 32th SICE Annual Conf (International Session)[C], 1993, 1067～1072
48. Kothare M V, et al. Robust Constrained Model Predictive Control Using Linear Matrix Inequalities[J]. Automatica, 1996, 32(10): 1361～1379
49. Papa Vasilou G C, et al. A Descent Algorithm for a Min-Max Problem in Model Predictive Control[A]. Proc 30th Conference on Decision and Control[C], Brighton, 1991, 1869～1870
50. Rossiter J A., Feasibility and Stability Results for Constrained Stable Generalized Predictive Control[J]. Automatica, 1995, 31 (6): 863～877
51. Zheng A, et al. Robust Stability of Constrained Model Predicitive Control[A]. Proc American Control Conference[C], 1993, 379～383
52. Allwright J C. On Min-max Model Predictive Control, Advances in Model Based Predictive Control[M]. Oxford University Press, 1994
53. Lee J. H, et al. Worst-Case Formulations of Model Predictive Control for Systems with Bounded Parameters[J]. Automatica, 1997, 33(5): 763～781
54. Allwright, J.C, Papavasiliou, G..C. On Linear Programming and Robust Model Predictive Control Using Impulse Response[J]. System and Control Letters. 1992, 18(3): 159～164
55．王永骥，徐桂英，涂健．水轮发电机组L~∞范数性能指标预测控制[J]．控制理论与应用，1991，8(4)：386～392
56. Tsypkin Y Z. Robust stochastic Control Using the Internal Model Principle and Internal??Model Control[J]. Int J Control, 1995, 61(4): 809～822
57. Vandeursen J M, et al. Internal Model Control with Improved Distarbance Rejection[J]. Int J Control, 1995, 62(4): 983～999
58. Gawthrop P J, et al. Emulator-based Control and Internal Model Control: Compelmentary Approaches to Robust Control Design[J]. Antomatica, 1996, 32(8): 1223～1227
59．张化光等．模糊广义预测控制及其应用[J]．自动化学报，1993，19(1)：9～17
60．李静如，李尚春．模糊预测控制及其应用研究．控制理论与应用[J]，1992，9(3)：283～286
61. Van Can H J L, et al. Neural models in Predictive Control[A]. Int. Conf. Computer Applications in Biotechnology[C]. Garmisch Germany, 1995, 95～100
62．冯思波等．基于神经网络技术的解耦及容错解耦控制策略[J]．信息与控制，1992，21(6)：363～368
63. Wang L X, et al. Generating Fuzzy Rules by Learning Form Example[J]. IEEE Trans on SMC, 1992, 22(6): 1414～1427
64. Wang L X, Design and Analysis of Fuzzy Identifiers of Nonlinear Dynamic Systems[J]. IEEE Trans on Auto Cont, 1995, 40(1): 11～23
65. Rechalet J. Model Predictive Heuristic Control: Applications to Industrial Processes[J]. Automatica, 1978, 17(5): 413～428
66. Cutler C R, Ramaker B L. Dynamic Matrix Control—A Computer Control Algorithm[A]. Proc JACC Sam Francisco[C], 1980
67. Rouhani R, Mehra R K. Model Algorithmic Control Basic Theoretical Properties[J]. Automatica, 1982, 18(4): 401～414
68. Garcia G E, Morshedi A M. Quadratic Programming Solution of Dynamic Matrix Control(QDMC)[J]. Chem Eng Commun, 1984, 46(1): 73～87
69. Grosclidier P. The IDCOM-M Control[A]. IFAC Workshop on Model-Based Control[C]. Atlanta GA, 1988
70. Clarke D W. Generalized Predictive Control—Part 1 and Part 2 Basic Algorithm[J]. Automatica, 1987, 23(2): 137～162
71. Lelic M A, Zarrop M B. Generalized Pole Placement Self-tuning Controller. Int J Control, 1987, 46(2): 547～56872. Ydstie B E. Extended Horizon Adaptive Control[A]. Budapest: Proe IFAC 9th World Congress[C], 1984, 2:911～915
73. De Keyser R M C. A Self-tuning Multi-Step. Predictor Application[J], Automatica, 1981, 117(1): 167～174
74. Garcia C E, Morari M. Internal Model Control, Ind, Engng Chem, Process Des. Dev., 1982, 21(2): 308～332,472～494
75. Brosilow C B. Joseph B. Inferential Control of Processes[J]. AICHE J, 1978, 24(3): 485～509
76. Kwon W H, Byun D G. Receding Horizon Tracking Control as a Predictive Control and its Stability Properties[J]. Int J Control, 1989, 50(5): 1807～1824
77．金元郁，顾兴源．改进广义预测控制算法[J]，信息与控制，1990，19(3)：8～13
78．金元郁，顾兴源．改进的多变量广义预测控制算法[J]．信息与控制，1990，19(6)：20～23
79．顾兴源，毛志忠．一种基于广义预测控制的极点配置白适应控制算法[J]．控制与决策，1992，7(3)：221～224
80. Cloud D J, Kouvaritakis B. Characteristic Decomposition and Multivariable Generalisation of Prelictive Self-tuning Control[J]. Proc IEE, 1988, 135(3): 165～181
81．吕剑虹，徐治皋，陈来九．特征结构下多变量预测控制系统的闭环反馈结构及其应用[J]．控制理论与应用，1992，9(2)：174～182
82. Pedro A. On Generalized Predictive Control: Two Alternative Formulations. Automatica, 1989, 25(5): 753～755
83. Visser H R. Unified Predictive Pole-placemend Controller: an Integrated Approach[J]. Control and Computer, 1989, 17(3): 71～74
84. Peng L. Tuning Generalized Predictive Control Using a Pole-placement Criterion[A]. Proc. American Control Conference[C]. 1992,2391～2395
85．孙茂相，王曙光，吴学曼，封锡盛，关玉林．广义预测极点配置前馈控制的一种算法[J]．信息与控制，1992，21(6)：338～342
86．徐立鸿，冯纯伯．加权多步预测控制[J]．自动化学报，1991，17(6)：658～668
87．徐立鸿，冯纯伯．多变量加权多步预测控制[J]．控制理论与应用，1992，9(3)：238～244
88．徐立鸿，冯纯伯．递推自适应加权多步预测控制[J]．自动化学报，1992，18(5)：??580～584
89．周德云．一种新的鲁棒多变量广义预测控制算法[J]．控制与决策，1992，7(6)：420～426
90. Irving E, et al. Adaptive Generalized Predictive Control with Multiple Reference Model. Lund[J]; 2th, IF AC Workshop on Adaptive systems in Control and Signal Processing, 1986
91. Gorez R. On a Generalized Predictive Control Algorithm[J]. System & Control Letters, 1987, (9): 369～377
92. Koh E K. Generalized Pole-placement for Systems Requiring Fast Sumpling[A]. Proc American Control Conference[C], 1992, 713～714
93. Chai T. Decoupling Design of Multivariable Generalized Predictive Control[J]. IEEE Proceedings, 141(3): 197～201
94．诸静，黄抗美．复杂系统的广义预测模糊控制．电工技术学报[J]，1997，12(2)：7～12
95. Kershenbaum L S, et al. An Extended Horizon Feedback/Feed for Ward Self-Tuning Controller[J]. AICHEJ, 1989, 35(11): 1835～1844
96. Linkens D A, Mahfoub M. Generalized Predictive Control with Feed for Ward (GPCF) for Multivaraible Anaesthesia[J], Int. Control, 1992, 56(5): 1039～1057
97. Song J J, Park S W. Neural Model Predictive Control for Nonlinear Chemical Processes[J], J Chemical Engineering of Japan, 1993
98．邹志云．新型自适应模型算法控制算式[J]．控制与决策，1988，3：11～16
99．古钟壁．具有误差预测修正的预测控制算法[J]．控制与决策，1992，7(6)：432～436
100．王伟．一种广义预测自适应的直接算法[J]．自动化学报，1996，22(3)：270～277
101．周德云，陈新海，佟明安．广义预测鲁棒自适应控制器[J]．控制与决策，1991，6(4)：265～270
102. Eishabei A, Dumont, G., Elnaggar, A. Stability and Convergence Analyses of an Adaptive GPC Based on State-Space Model. Int J Control[J], 1995, 61(1): 193～210
103. Yoon T, Clarke, D.W. Adaptive Predictive Control of the Benchmark Plant[J]. Automatica, 1994, 30(4): 621～628
104. De Nicolao G. An Adaptive Predictive Regulator with Input Saturations[J]. Automatica, 1996, 32(4): 597～601
105. Chisci L. Indirect and Implicit Adaptive Control of the Benchmark Plant[J].??An-tomatic, 1994, 30(4): 577～584
106. Mossca E. Adaptive Predictive Control with Meansquare Input Constraints[J], Automatica, 1992, 28(3): 593～597
107. Datta A. Adaptive Internal Model Control: Design and Stability Analysis[J]. Automatica, 1996, 32(2): 261～266
108. Lee J Y. Receding Horizon Predictive Control for the Continuoustime Systems[A]. Proc 32th SICE Annual Conf (International Session)[C], 1993, 1067～1072
109. Kothare M V. Robust Constrained Model Predictive Control Using Linear Matrix Inequalities[J]. Automatica, 1996, 32(10): 1361～1379
110. Papa Vasilou G C. A Descent Algorithm for a Min-Max Problem in Model Predictive Control[A]. Proc 30th Conference on Decision and Control[C], Brighton, 1991, 1869～1870
111. Rossiter J A. Feasibility and Stability Results for Constrained Stable Generalized Predictive Control. Automatica[J], 1995, 31 (6): 863～877
112. Zheng A. Robust Stability of Constrained Model Predicitive Control[A]. Proc American Control Conference[C], 1993,379～383
113. Allwright J C. On Min-max Model Predictive Control, Advances in Model Based Predictive Control[M]. Oxford University Press, 1994
114. Lee J. H. Worst-Case Formulations of Model Predictive Control for Systems with Bounded Parameters[J]. Automatica, 1997, 33(5): 763～781
115. Allwright J C. On Linear Programming and Robust Model Predictive Control Using Impulse Response[J]. System and Control Letters. 1992, 18(3): 159～164
116. Tsypkin Y Z. Robust stochastic Control Using the Internal Model Principle and Internal Model Control[J]. Int J Control, 1995, 61(4): 809～822
117. Vandeursen J M. Internal Model Control with Improved Distarbance Rejection[J]. Int J Control, 1995, 62(4): 983～999
118. Gawthrop P J. Emulator-based Control and Internal Model Control: Compelmentary Approaches to Robust Control Design[J], Antomatica, 1996, 32(8): 1223～1227
119．张化光．模糊广义预测控制及其应用[J]．自动化学报，1993，19(1)：9～17
120．李静如，李尚春．模糊预测控制及其应用研究．控制理论与应用[J]，1992，9(3)：283～286121. Van Can H J L. Neural models in Predictive Control. Int. Conf. Computer Applications in Biotechnology[J], Garmisch Germany, 1995, 95～100
122．冯思波．基于神经网络技术的解耦及容错解耦控制策略[J]，信息与控制，1992，21(6)：363～368
123．张向东，孙薇，金锦良．多层前馈模糊神经网络进行图像识别[J]，计算机科学与应用，2001，18(5)：1-4
124．王玉涛，颜华，周建常，王师．一种自组织模糊神经元网络控制学习方法及其应用[J]，东北大学学报，1998，19(2)：191-194
125．古钟璧，王祯学，胡丽莉．模糊神经网络自适应控制[J]，四川大学学报，1998，35(1)：71-76
126．廖俊，朱世强，林建亚．一种新型的模糊神经网络控制器[J]，仪器仪表学报，1998，19(2)：163-167
127. Chin-Teng Lin, Ya-Ching Lu. A neural fuzzy system with fuzzy supervised learning[J], IEEE Transaction on System, Man, and Cybernetics-Part B: Cybernetics, 1996, 26(5): 744-763
128．周明，孙树栋，遗传算法的原理与应用[M]，1999年，国防工业出版社，北京，11-13
129. WANG, Y.Q., XU, L., YU, X.L., WANG, J.H., GU, S.S. Survey on Modern Optimization Techniques. International Journal of Information and Systems Sciences
130．徐容，徐用懋，兰鸿森．多层前向网络的研究——遗传BP算法和结构优化策略[J]，自动化学报，1997，23(1)：43-49
131．穆文全，廖晓峰，虞厥邦．基于遗传算法和BP算法的多层感知机杂交训练算法[J]，电子科学学刊，1997，19(2)：190-19
132. Davis, L. Adapting Operator Probabilities in Genetic Algorithms[A], In: Proceedings of the third international conference on Genetic algorithms, Morgan Kaufmann Publishers Inc., San Francisco[C], CA, USA, 1989, 61-69
133. Kenneth Alan De Jong. An analysis of the behavior of a class of genetic adaptive systems[D], Univ. Michigan, Ann Arbor, MI, 1975
134. Whitley, D., Starkweather, T. Genitor Ⅱ: A Distributed Genetic Algorithm[J], J. Expt. Ther. Intell., 1990, 2, 189-214
135. Wang Chi-Hsu, Liu Han-Leih, and Lin Chin-Teng. Dynamic Optimal Learning Rates of a Certain Class of Fuzzy Neural Networks and its Applications with Genetic??Algorithm[J], IEEE Transaction on System, Man, and Cybernetics-Part B: Cybernetics, 2001, 31(3):467-475
136. Frank H. F., Leung, H. K. Lam, S. H. Ling and Peter K.S. Tam. Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm[J], IEEE Transactions on Neural Network, 2003, 14(1): 79-88
137. Hanaki, Y., Hashiyama, T. and Okuma, S. Accelerated evolutionary computation using fitness estimation[A], in Proc. IEEE Int. Conf. Syst., Man, Cybern.[C], 1999, 1, 643-648
138. Kenneth Alan De Jong. An analysis of the behavior of a class of genetic adaptive systems[J], Ph.D. dissertation. Univ. Michigan, Ann Arbor, MI, 1975
139. Wang L X. Generating Fuzzy Rules by Learning Form Example[J], IEEE Trans on SMC, 1992, 22(6): 1414～1427
140. Wang L X. Design and Analysis of Fuzzy Identifiers of Nonlinear Dynamic Systems[J], IEEE Trans on Auto Cont, 1995, 40(1): 11～23
141．翁学义，赵光宙．非线性预测控制方法的研究[D]，杭州：浙江大学，1998
142. Song J J, Park S W. Neural Model Predictive Control for Nonlinear Chemical Processes[J], J Chemical Engineering of Japan, 1993
143. Takagi T, Sugeno M. Fuzzy Identification of Systems and its Applications to Modeling and Control[J], IEEE Trans. SMC, 1985 (15): 116～132
144. Wang L X. Design and Analysis of Fuzzy Identifiers of Nonlinear Dynamic Sys tems[J]. IEEE Trans on Auto Cont, 1995, 40(1): 11～23
145．赵振宇，徐用懋．模糊理论和神经网络的基础与应用[M]，北京：清华大学出版社，1996，156～174