水轮机调节系统控制策略及模型辨识方法研究
|
摘要
水电机组系统是具有时变、非线性、非最小相位等特性的复杂系统,其控制的可靠性是水电厂安全运行的关键。为了提高系统的控制性能,设计出控制效果更好的水轮机调节器,在全面总结现有水电机组系统控制策略和辨识方法成果的基础上,结合水电机组控制系统特性,引入智能控制系统,对水电机组系统辨识和控制方法做了深入研究,提出了具体的改进措施和智能控制器的设计方法,并给出了仿真结果。
在分析水电机组经典数学建模和常规人工神经网络建模方法基础上,提出了基于Takagi-Sugeno型的ANFIS网络进行水电机组辨识方法,并针对该方法在误差较小时训练网络参数收敛速度慢的问题,采用拟Newton算法和梯度下降混合学习算法进行参数的训练学习,使ANFIS辨识网络具有很好的实时性,对水电机组进行辨识提供了一种新途径。
针对常规PID控制存在的问题,详细分析了现有的PID控制优化方法,并对这些优化方式进行了设计和仿真;结合仿真实验结果,分析了其优缺点,为选择较好的优化PID方法提供了依据,在此基础上提出了采用经过模糊整定的PID控制与模糊控制并联揉合构成的水电机组控制器,仿真结果验证了其有效性。
为了提高系统控制性能,设计了水电机组模糊神经网络控制器,详细介绍了其控制器结构、模型和学习方法。为了克服基于误差反传的模糊神经网络控制器学习过程容易产生振荡和收敛速度慢的缺点,提出了采用自适应学习训练算法。为确保模糊神经网络控制器学习过程的稳定和收敛,采用Lyapunov理论对模糊神经网络学习参数进行了优化,仿真结果验证了其有效性。
针对模糊神经网络设计和仿真过程中的结构参数选择具有一定的主观性和试探性的问题,提出将软计算方法应用于水电机组控制器设计中,设计了基于模糊推理系统、神经网络、遗传算法相融合的水电机组控制器,给出了控制器的结构和设计方法,针对遗传算法优化中存在的早熟等问题,提出了采用改进遗传算法进行优化的方法。为了防止水电机组大扰动时模糊神经网络学习速度慢、易陷入局部最优引起控制效果不佳或引起不稳定现象的发生,控制器中并联了监督控制器,给出了仿真结果。
全面系统地总结了本文的工作和研究成果,并指出了有待改进的地方和需进一步开展的工作。
The hydroelectric generating system of water power plant is a high-order, non-linear, with time-variable and non-minimal phase properties system. The reliable control of hydroelectric generating system is a key of water power plant safe running. In order to research better intelligent hydro turbine governor to improve control performance, the dissertation summarizes the existing control strategies and identifying methods. Combining the character of complicated hydroelectric generating, intelligent control strategies and identifying methods have been researched and some concrete measures of improvement and designing methods of intelligent governor are put forward. The corresponding application simulating results are given. After analyzing general math modeling and conventionality artificial neural network modeling of hydroelectric generating, the ANFIS network based on Takagi-Sugeno is put forward to identify the character of hydroelectric generating. To settle its slow convergence problem in turning network parameters when error is less,analogy Newton algorithm and gradient-descent mix algorithm are adopted to turning network parameters , which bright excellent real-time character and provided a brand new way to the identification of hydroelectric generating.
According to the problem existing in the regular PID control, some optimizing methods of PID control are further analyzed and their relevant design and simulating results are given. Then the advantages and disadvantages are discussed based on simulating experiment results, which provides convincing proves for better choice PID optimizing methods. Based on all above work, mix control strategy based on PID control with fuzzy optimizing and fuzzy control is put forward. The simulating results prove its validity.
To improve the controllability of bigger water power hydroelectric generating system,a new hydroelectric generating controller is designed based on fuzzy neural network. The structure, model and study approaches are introduced in detail in the dissertation as well. To handle the problem that slow convergence speed or shake brought by using back-propagation method only in learning,the self-adapt learning method is used to turning parameters. To ensure the stability and convergence of fuzzy neural network controller during the learning process,network parameters are chosen based on Lyapunov theory. The simulating results proved its validity.
As for choosing parameters of the fuzzy neural network controller having some subjectivity and probing, soft-computing method based on fuzzy reference system, neural network and genetic algorithm is put forward to control hydroelectric generating. The structure and design of controllers are given. The improved optimizing method of genetic algorithm is put forward to solve the problem that existed in the optimizing of genetic algorithm. Control monitor is used to avoid brought the problem bad performance or shake when fuzzy neural network optimizing in bigger work condition change. The simulating results of designed intelligent controller are given.
Finally, the dissertation summarizes all the works and results achieved in this dissertation. The further research works to be developed are also put forward.
在分析水电机组经典数学建模和常规人工神经网络建模方法基础上,提出了基于Takagi-Sugeno型的ANFIS网络进行水电机组辨识方法,并针对该方法在误差较小时训练网络参数收敛速度慢的问题,采用拟Newton算法和梯度下降混合学习算法进行参数的训练学习,使ANFIS辨识网络具有很好的实时性,对水电机组进行辨识提供了一种新途径。
针对常规PID控制存在的问题,详细分析了现有的PID控制优化方法,并对这些优化方式进行了设计和仿真;结合仿真实验结果,分析了其优缺点,为选择较好的优化PID方法提供了依据,在此基础上提出了采用经过模糊整定的PID控制与模糊控制并联揉合构成的水电机组控制器,仿真结果验证了其有效性。
为了提高系统控制性能,设计了水电机组模糊神经网络控制器,详细介绍了其控制器结构、模型和学习方法。为了克服基于误差反传的模糊神经网络控制器学习过程容易产生振荡和收敛速度慢的缺点,提出了采用自适应学习训练算法。为确保模糊神经网络控制器学习过程的稳定和收敛,采用Lyapunov理论对模糊神经网络学习参数进行了优化,仿真结果验证了其有效性。
针对模糊神经网络设计和仿真过程中的结构参数选择具有一定的主观性和试探性的问题,提出将软计算方法应用于水电机组控制器设计中,设计了基于模糊推理系统、神经网络、遗传算法相融合的水电机组控制器,给出了控制器的结构和设计方法,针对遗传算法优化中存在的早熟等问题,提出了采用改进遗传算法进行优化的方法。为了防止水电机组大扰动时模糊神经网络学习速度慢、易陷入局部最优引起控制效果不佳或引起不稳定现象的发生,控制器中并联了监督控制器,给出了仿真结果。
全面系统地总结了本文的工作和研究成果,并指出了有待改进的地方和需进一步开展的工作。
The hydroelectric generating system of water power plant is a high-order, non-linear, with time-variable and non-minimal phase properties system. The reliable control of hydroelectric generating system is a key of water power plant safe running. In order to research better intelligent hydro turbine governor to improve control performance, the dissertation summarizes the existing control strategies and identifying methods. Combining the character of complicated hydroelectric generating, intelligent control strategies and identifying methods have been researched and some concrete measures of improvement and designing methods of intelligent governor are put forward. The corresponding application simulating results are given. After analyzing general math modeling and conventionality artificial neural network modeling of hydroelectric generating, the ANFIS network based on Takagi-Sugeno is put forward to identify the character of hydroelectric generating. To settle its slow convergence problem in turning network parameters when error is less,analogy Newton algorithm and gradient-descent mix algorithm are adopted to turning network parameters , which bright excellent real-time character and provided a brand new way to the identification of hydroelectric generating.
According to the problem existing in the regular PID control, some optimizing methods of PID control are further analyzed and their relevant design and simulating results are given. Then the advantages and disadvantages are discussed based on simulating experiment results, which provides convincing proves for better choice PID optimizing methods. Based on all above work, mix control strategy based on PID control with fuzzy optimizing and fuzzy control is put forward. The simulating results prove its validity.
To improve the controllability of bigger water power hydroelectric generating system,a new hydroelectric generating controller is designed based on fuzzy neural network. The structure, model and study approaches are introduced in detail in the dissertation as well. To handle the problem that slow convergence speed or shake brought by using back-propagation method only in learning,the self-adapt learning method is used to turning parameters. To ensure the stability and convergence of fuzzy neural network controller during the learning process,network parameters are chosen based on Lyapunov theory. The simulating results proved its validity.
As for choosing parameters of the fuzzy neural network controller having some subjectivity and probing, soft-computing method based on fuzzy reference system, neural network and genetic algorithm is put forward to control hydroelectric generating. The structure and design of controllers are given. The improved optimizing method of genetic algorithm is put forward to solve the problem that existed in the optimizing of genetic algorithm. Control monitor is used to avoid brought the problem bad performance or shake when fuzzy neural network optimizing in bigger work condition change. The simulating results of designed intelligent controller are given.
Finally, the dissertation summarizes all the works and results achieved in this dissertation. The further research works to be developed are also put forward.
引文
[1] Ye Luqing. Field Test and Operation of a Duplicate Multiprocessor-based Governor for Water Turbine and Its Further Development. IEEE Trans on Energy Conversion, June 1990, 5(2):225~231
[2] 叶鲁卿.水力发电过程控制—理论、应用及其发展.第一版.武汉:华中科技大学出版社,2002.7~333
[3] 李朝晖,郭江.调速器液压系统的故障诊断及可靠控制策略.水电能源科学,1999,17(3):40~44
[4] 李朝晖.微计算机控制系统可靠性理论研究及其在水轮机调节中的应用.[博士学位论文].武汉:华中科技大学图书馆,1991
[5] 魏守平,罗萍,张富强.水轮机调节系统的适应式变参数控制. 水电能源科学,2003,21(1):32~35
[6] Scott C. Bonnert, L. Wozniak. Adaptive Speed Control of Hydrogenerators by Recursive Least Squares Identification Algorithm. IEEE Trans. On Energy Conversion, March 1995, 10(1):162~168.
[7] 蔡维由,陈光大,刘炳文. 水轮机调速器的极点配置法设计及自适应控制. 大电机技术,1995,6:47~56
[8] Ye Luqing, Wei Shouping, Li Zhaohui et al. An Intelligent Self-Improving Control Strategy with a Variable Structure and Time-Varying Parameters for Water Turbine. La Houille Blanche, 1989, 6:463~475
[9] Ye Luqing. Variable Structure Control and Its Applications to Hydroelectric Generating Unit. Journal of Hydroelectric Engineering, 1992, 1:28~38
[10] Lansbeerry J E, Wozniak L. Optimal hydrogenerator governor tuning with a genetic algorithm. IEEE Trans on Energy Conversion, 1992, 7:623~628
[11] Jin Jiang. Design of an Optimal Robust Governor for Hydraulic Turbine Generating Units. IEEE Trans. on Energy Conversion, 1995, 10(1): 188~194
[12] O.P. Malik,Y. Zeng. Design of a Robust Adaptive Controller for a Water Turbine Governing System. IEEE Trans. on Energy Conversion, 1995, 10(2):354~359
[13] 程远楚,水电机组智能控制策略与调速励磁协调控制的研究.[博士学位论文].武汉:华中科技大学图书馆,2002
[14] 孙增沂.智能控制理论与技术.北京:清华大学出版社,第一版,1997.54~87
[15] 李人厚.智能控制理论和方法.西安:西安电子科技大学出版社,第一版,1999.23~76
[16] 蔡自兴.智能控制.北京:电子工业出版社,第二版,2004.12~56
[17] Panos J. Antsaklis. Intelligent Learning Control. IEEE Control System Magazine, 1995, 15(3):5~7
[18] Gellent Stephen I. Neural Network Learning and Expert System. Cambridge Massachusetts, London, England, The MIT Press, 1993,45~76
[19] Yuan Zeng-Ren, Guo Xin-Gang. Back-Propagation Neural Networks for the Inverse Control of Discrete-Time Nonlinear Plant. Advances in Modeling & Analysis, C, AMSE Press, 1994, 40(4):13~23
[20] Huang, S. H, ZHANG, H. C. Artificial Neural Networks in Manufacturing: Concepts, Applications and Perspectives. IEEE Transactions on Components, Packaging and Manufacturing Technology, Part A, 1994, 17(2): 212~228
[21] Filev D P, Yager R R. On the analysis of fuzzy logic controllers. Fuzzy Sets and Systems, 1994, 68:39~66
[22] Mu-Song Chen, Shinn-Wen Wang. Fuzzy clustering analysis for optimizing fuzzy membership function. Fuzzy Sets and Systems, 1999, 103:239~254
[23] F.Aminzadeh and M.Jamshidi. Soft Computing-Fuzzy Logic, Neural Networks, and Distributed Artificial Intelligence. Northern Ireland, Prentice-Hall, 1994,23~78
[24] L.A.Zadeh. Fuzzy Logic, Neural Networks, and Soft Computing. Comm. ACM, 1994, 37(3):77~84
[25] E.Mizutani, H.Takagi, and D.M.Auslander. A cooperative system based on soft computing methods to realize higher precision of computer color recipe prediction. In Proceedings of Application on OE/Arospace Sensing and Dual Use Photonics, 1995, 303~314
[26] 魏守平.水轮机控制工程.武汉:华中科技大学出版社,2005,13~28
[27] 方红庆,孙美凤,沈祖诒.水轮机调节系统控制策略综述.人民长江,2004,1:45~49
[28] 景雷.水轮机调节系统智能控制理论及应用研究.[博士学位论文].武汉:华中科技大学图书馆,1997.
[29] 沈祖诒.水轮机调节.北京:水利水电出版社.1998,35~58
[30] L. M. Hovey. Optimum adjustment of Hydro Governors on Manitoba Hydro System. AIEE Trans. Part III, 1962, 81:581~587
[31] L. D. Murphy. A Digital Governor for Hydrogenerators. IEEE Trans. on Energy Conversion, 1988, 3(4):780~784
[32] 魏守平,罗萍,张富强.水轮机调节系统的适应式变参数控制. 水电能源科学,2003,4:64~67
[33] 魏守平.水轮机调节系统的适应式变参数调节.大电机技术,1985,5:48~54
[34] 魏守平,卢本捷.水轮机调速器的 PID 调节规律.水力发电学报,2003, 4:112~118
[35] G. Orelind. Optimal PID Gain Schedule for Hydrogenerators—Design and Application. IEEE Trans. on Energy Conversion, 1989, 4(3):300~317
[36] Lansbeerry J E, Woznliak L. Adaptive hydrogenerator governor tuning with a genetic algorithm. IEEE Trans on Energy Conversion, 1994, 9(1):179~183
[37] O.P. Malik. An Intelligent Self-Improving Control Strategy with a Variable Structure and Time-Varying Parameters for Water Turbine. La Houille Blanche, 1989, 6:463~475
[38] Scott C. Bonnert, L. Wozniak. Adaptive Speed Control of Hydrogenerators by Recursive Least Squares Identification Algorithm. IEEE Trans. On Energy Conversion, 1995, 10(1):162~168
[39] 陈光大,刘炳文,蔡维由等.基于测试系统频率的自适应水轮机调速器.水力发电学报,1993,2:34~37
[40] Ye Luqing. Multivariable Adaptive Control and How to achieve it with a Microprocessor. Proceedings of the International Symposium on Applied Control and Identification, Copenhagen, Denmark, 1983, 20~25
[41] Ye Luqing. Multiprocessor-Based Fault Tolerant Adaptive Parameter-Optimized Governor for Water Turbine. Proceedings of Mini and Microcomputers and Their Applications, Saint Feliu, Spain, June 1985, 57~62
[42] Jing Lei, Ye Luqing. An Intelligent discontinuous control strategy for hydroelectric generating unit. IEEE Trans. on Energy Conversion, 1999, 13(1):84~89
[43] 王柏林.水轮发电机组的模型参考自适应控制.自动化学报,1987,13(6): 408~415
[44] 蔡维由,陈光大,刘炳文.水轮机调速器的极点配置法设计及自适应控制.大电机技术,1995,6:78~81
[45] 孟佐宏,蔡维由,陈光大等.水轮机调节系统最优鲁棒极点配置调速器的设计.大电机技术,2000,4:32~35
[46] Jin Jiang. Design of an Optimal Robust Governor for Hydraulic Turbine Generating Units. IEEE Trans. on Energy Conversion, 1995, 10(1):188~194.
[47] 余刃.智能控制-维护-管理集成系统框架下预知维护理论与应用.[博士学位论文]. 武汉:华中科技大学图书馆,2000
[48] Ye Luqing. Variable Structure Control and Its Applications to Hydroelectric Generating Unit. Journal of Hydroelectric Engineering, 1992, 1:28~38
[49] Ye Luqing. Variable Structure and Time-Varying Parameter Control for Hydroelectric Generating Unit. IEEE Trans. on Energy Conversion, 1989, 4(3):293~299
[50] Ye Luqing. An Intelligent Self-Improving Control Strategy with a Variable Structure and Time-Varying Parameters for Water Turbine. La Houille Blanche, 1989, 6:463~475
[51] Louis Wozniak, Daniel J. Bitz. Load-Lever-Sensitive Governor for Speed Control of Hydrogenerators. IEEE Trans. on Energy Conversion, 1988, 3(1):78~84
[52] 金和平.多变量预测控制及其在水电生产过程中的应用研究.[博士学位论文] .武汉:华中理工大学图书馆,1993.
[53] 刘欣,尹绍清,肖顺达.模糊自适应 Smith 预估控制及其应用.控制理论与应用.1993,73:225~228
[54] 陈启卷.系统辨识和智能化控制及在水电机组中的应用.[博士学位论文].武汉:武汉大学图书馆,1999.
[55] M. Lown, E. Swidenbank, B. W. Hogg. Adaptive Fuzzy Logic Control of a Turbine Generator System. IEEE Trans. on Energy Conversion, 1997, 12(4):394~399
[56] Braae M, Rutherford D A. Theoretical and Linguistic Aspects of Fuzzy Logic Controller. Automatica, 1979, 15(5):553~577
[57] J. J. Buckly. An Fuzzy Expert Systems. Fuzzy Sets and Systems, August 1986, 20(1):1~16
[58] 龚崇权,肖惠民,蔡维由. 模糊控制在水轮机调节应用中运行模式的探讨. 长沙电力学院学报(自然科学版),2001,16(3):52~54
[59] 林富华,沈恩源,林建亚等. 模糊 PID 技术在水轮机调节系统中的应用. 动力工程,1996,2:45~48.
[60] Daijin Kim. A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation. Fuzzy Set and Systems, 2002, 125:93~104
[61] Zhang Y., Chen G.P., Malik O.P., Hope G.S. An Artificial Neural Network based Adaptive Power System Stabilizer, IEEE Transaction on Energy Conversion, 1993, 8(1):71~77
[62] Djukanovic, M.B, Calovic, M.S,Vesovic, B.V. Neuro-fuzzy Controller of Low Head Hydropower Plants using Adaptive-network based Fuzzy Inference System, IEEE Transaction on Energy Conversion, 12(4), 1997: 375 ~381.
[63] 郭创新,梁年生,叶鲁卿.基于神经网络实现水轮机自学习 PID 调节.水力发电学报,1997,1:36~39
[64] 景雷,叶鲁卿,周泰经. 基于在线学习的水轮发电机组智能控制系统研究.电力系统自动化,1997,4:43~47
[65] 蔡维由,刘海锋,陈光大等. 水轮机调节系统的模糊神经控制.长江科学院院报,2003,20(2):45~49
[66] 龚崇权. 基于遗传算法的水轮机调节系统研究.[博士学位论文].武汉:武汉大学图书馆,2002
[67] 刘乐星,毛宗源.水轮机的 GA-PID 控制器研究.电力系统自动化,1997,12:65~69
[68] 孟安波, 叶鲁卿, 殷豪.遗传算法在水电机组调速器 PID 参数优化中的应用.控制理论与应用,2004,21(3):398~407
[69] 南海鹏,罗兴绮,余向阳.基于遗传算法的水轮机智 PID 调速器研究. 水力发电学报,2004,23(2):107~112
[70] 龚崇权,蔡维由,肖惠民.基于遗传算法的水轮机调节系统最优参数整定.电力系统自动化,2002,26(15):57~59
[71] 廖忠,沈祖诒. 基于正交交叉操作的遗传算法及在水轮机调速器参数优化中的应用.大电机技术,2003,5:61~64
[72] 余向阳,南海鹏,杨晓萍. 基于遗传算法的水轮机模糊自适应调速器研究. 大电机技术,2004,1:63~67
[73] Ra_k A. Aliev, Bijan Fazlollahi, Rustam M. Vahidov. Genetic algorithm-based learning of fuzzy neural networks. Part 1: Feed-forward fuzzy neural networks, Fuzzy Sets and Systems, 2001, 118:351~358
[74] Cheng-Jian Lin. A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems, 2004, 143:311~333
[75] Zadeh L.A. From circuit theory to system theory. Proc.IRE, 1962, 50(5):856~865
[76] Chin-Teng Lin. Neural Network-Based Fuzzy Logic Control and Design System, IEEE Trans. on Computer, 40(12), Dec. 1991: 1320~1336.
[77] Ljung L. Convergence analysis of parametric identification methods. IEEE Trans on Automatic Control, 1978, 23:770~783
[78] 方崇智,萧德云.过程辨识.北京:清华大学出版社,1988,34~46
[79] 黄正良,吴坚,万百五. 辨识 Wiener 模型的一种新方法.控制理论与应用.1996, 13 (3):326-332
[80] Billings S.A, Fakhovri S. V. Nonlinear System Identification Using the Hammerstein Model. Int.J, System Science, 1979, 10(5):23~39
[81] 孙昀,沈祖诒.基于模糊神经网络的水力机组模型辨识.河海大学学报,2000,2:32~35
[82] Rainer Palm, Robust Control by Fuzzy Sliding Mode, Automatica, 30(9), 1994:1429~1434
[83] Leonlaritis I.J, Billings S.A. Input-output parametric models for nonlinear systems Part II: stochastic nonlinear systems. Int.J.Control,1985,41(2):329~334
[84] Chen S, Billings S.A. Representations of nonlinear systems: the NARMAX model.Int. J. control, 1989, 49(3):1013~1032
[85] Billings S.A, Chen S. Extended model set, global data and threshold model identification of severely nonlinear systems. Int.J.Control, 1989, 50:1897~1892
[86] Chen S, Billings S.A. Neural network for nonlinear dynamic system modeling and identification .Int.J.control,1992, 5: 319~346
[87] Chen S, Billings S.A, Grant P. Nonlinear system identification using neural network Int.J. control, 1990, 51:1191~1214
[88] Narendra K.S, Parthasarathy L. Identification and Control of Dynamical Systems Using Neural Net works. IEEE Trans On Neural Networks,1990,1(1):4~27
[89] 刘延年,冯纯伯. 用神经网络进行非线性离散动态系统辨识的可行性. 控制理论与应用,1994,11(4):413~420
[90] 任雪梅,高为炳.基于神经网络非线性系统辨识和控制的研究.控制理论与应用, 1995,12(2):147~153
[91] 鲍晓红,贾英民.用动态隐层的前馈网辨识非线性系统.自动化学报.1997,23(5):689~693.
[92] 王淑青,李朝晖. 基于自适应模糊神经网络的水轮机特性辨识研究.武汉大学学报,2006,2:74~76
[93] Fukuda Toshio, Ghibata Takanori. Theory and Application of Neural Networks for Industrial Control Systems. IEEE Transaction on Industrial Electronics, December 1992,39(6):472~489
[94] 程远楚,叶鲁卿,蔡维由.水轮机特性的神经网络建模. 华中科技大学学报, 2003,6:68~70
[95] 常江,谢云敏.混流式水轮机神经网络模型非线性仿真.中国农村水利水电, 2004,6:75~77
[96] Jiang Chang, Yan Peng. Movable Propeller Turbine Neural Network Model and Nonlinear Simulation. In: Institute of Electrical and Electronics Engineers Inc, The Fourth International Conference on Machine Learning and Cybernetics. Baoding. Hebei Univeristy. 2005, 1220~1225.
[97] 郭君,董朝霞.基于神经网络的水轮发电机组的建模分析.电力系统及其自动化学报,2003,12:37~41
[98] Vila, M.A., Cubero, J.C., Medina, J.M., Pons, O. Soft computing: a new perspectivefor some data mining problems. Vistas in Astronomy, 1997, 41(3):379~386
[99] Arnold F. Shapiro. The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance: Mathematics and Economics, August, 2002, 131:115~131
[100] 权太范.模糊控制技术在控制中的应用现状及前景.控制与决策,1988,3(1):59~62
[101] A. MUHAMMAD, BARGIELA. Fuzzy and Evolutionary Modelling of Nonlinear Control Systems. Mathematical and Computer Modelling, 2001, 33: 533-551
[102] Linkens D A, Nie J. Constructing rule-bases for multivariable fuzzy control by self-learning, PartⅠ : System structure and learning algorithms. Int J Systems Sci, 1993, 24(1):111~127
[103] Ronald R. Yager. Implementing fuzzy logic controllers using a neural network framework, Fuzzy Sets and Systems, 1999, 100:133~144
[104] Chen C L., Chen W C. Self-organizing Neural Control Systems Design for Dynamic Process. International Journal of System Science, 1993, 24(8):1487~1507
[105] L.A. Zadeh. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Computing, 1998, 2:23~25
[106] L A. Zadeh. Fuzzy Set. Information and control, 1965, 8(2):338~358
[107] L.A. Zadeh. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics,1973, 3:28~44
[108] Tobi T. The application of fuzzy control to a coke oven gas cooling plant. Fuzzy Sets and Systems, 1992, 46:373~381
[109] Mamdani E H. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc IEEE, 1974,121:1585~1588
[110] Gupta M M. Multivariable structure of fuzzy control systems. IEEE Trans Syst, Man, Cybern, 1985, 15(2):260~271
[111] Gegov A. Multilevel intelligent fuzzy logic control of oversaturated urban traffic networks. Int J Systems Sci,1994, 25(6):967~978
[112] Khee F V D, Linkens D A, Nie J. Constructing rule-bases for multivariable fuzzy control by self-learning, Part Ⅱ: Rule-base formation and blood pressure control application. Int J Systems Sci, 1993, 24(1):129~157
[113] E.B.Baum and F. Wilczek. Supervised learning of probability distribution by neural networks. Neural information processing systems, 1988, 23:52-61
[114] Zhang Huaguang, Li Longcai, Bien Zeungnam. A multivariable generalized predicative control approach based on T-S fuzzy model. Int Journal of Intelligent & Fuzzy Systems, 2000, 9(3):169~190
[115] H. A. E de Bruin, B.Roffel. A new identification method for fuzzy linear models of nonlinear dynamic systems, Journal of Process Control, 1996, 6:277~293
[116] K.J. Hunt, D. Sbarbaro, R. Zbikowski, P.J. Gawthrop. Neural networks for control systems-a survey, Automatica, 1992, 28:1083~1112
[117] J.-S. Roger Jang. Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In Proceedings of the ninth National Conference on Artificial Intelligence(AAAI-91), 1991, 762-767
[118] J.Moody,C.Darken. Fast Learning in Network of Locally-tuned Processing Units. Neural Computation, 1989,1(2):281~294
[119] J.J.Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, USA, 1982, 79:2554~2558
[120] Zurada. Jacek M. Introduction to Artificial Neural Systems, New York: West Publishing Company. 1992, 59~74
[121] Simpson Patrick K. Artificial Neural Systems Foundation, Paradigms, Applications and Implementations. Inc. APPENDIX. Pergamon Press, 1990
[122] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation in parallel distributed processing. Rumelhart D e and McClelland J L, Eds. Cambridge, MA: MIT Press, 1986, 1:318~360
[123] D.S. Brommhead and D.Lowe. Multivariate functional interpolation and adaptive networks, Complex Systems, 1988, 2:321~355
[124] Min Han, Jianhui Xi. Efficient clustering of radial basis perception neural network for pattern recognition. Pattern Recognition. 2004, 37:2059~2067
[125] Caizhong Tian, Takao Fujii. Nonlinear system identification of rapid thermal processing. Control Engineering Practice, 2005, 13:681~687
[126] Ahmed Rubaai, Raj Kotaru. Adaptation Learning Control Scheme for a High-Performance Permanent-Magnet Stepper Motor Using Online Random Training of Neural Networks, IEEE Transactions on Industry Applications, 2001, 37(2):495~504
[127] Shuzhi Sam Ge, Jin Zhang, Tong Heng Lee. Adaptive Neural Network Control for a Class of MIMO Nonlinear Systems with Disturbances in Discrete-Time. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, August 2004, 34(4):1630~1645
[128] K.J.Hunt, D Sbarbao. Neural Networks for Nonlinear Internal Model Control. IEE Proc.-D,1991, 139(5):431~438
[129] M.M. Awais. Application of internal model control methods to industrial combustion. Applied Soft Computing , 2005, 5:223~233
[130] Flanagan J. Randall, Vetter Philipp, Johansson, Roland S. Prediction PrecedesControl in Motor. Learning Current Biology, January 2003, 13:146~150
[131] Cheng-Jian Lin. A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems, 2004, 143:311~333
[132] Rafik A. Aliev, Bijan Fazlollahi, Rustam M. Vahidov. Genetic algorithm-based learning of fuzzy neural networks. Part 1:feed-forward fuzzy neural networks, Fuzzy Sets and Systems, 2001, 118:351~358
[133] Yie-Chien Chen, Ching-Cheng Teng. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems, 1995, 73:291~312
[134] L. Jiexing, Z. Yun, F. Xi. An improved closest cluster learning algorithm. Control Theory Appl. 2000, 17:735~738
[135] Y. Hayashi, J. Buckley, E. Czogola. Fuzzy neural networks with fuzzy signals and weights. Internat. J. Intell. Systems, 1993, 8:527~537
[136] M.Srinivas and L.M.Patnaik. Genetic algorithms: A survey. IEEE Computer, June 1994, 17~26
[137] Bala J. Shape Analgsis using Hybird Learning. Pattern Recognition,1996, 29(8):1323~1333
[138] K.A.De Jong. Learning with Genetic Algorithm: An Overview. Machine Learning, March 1988, 3:121~138
[139] Kuncheva L I. Fitness Functions in Editing K-NN Reference Set by Genetic Algorithms. Pattion,1997,30(6): 1041~1049
[140] Bhanu B, Lee S, Ming J. Self-Optimizing Image Segmentation System Using a Genetic Algorithm. In: Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, 1991, 362~369
[141] Beasley D, Bull D R. A Sequencing Niche Technique for Multimodal Function Optomization. Evolutionary Computation, 1993, 1(2):101~125
[142] Koji Shimojima. Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm. Fuzzy Sets and Systems, 1995, 71:295~309
[143] Montana,D.J. Automated parameter tuning for interpretation of synthetic images. Handbook of genetic algorithms, VNR Computer Libiary,1989
[144] Tien-Chi Chen, Tsong-Terng Sheu. Model Reference Neural Network Controller for Induction Motor Speed Control. IEEE Tran. on Energy Conversion, 2002, 17(2):157~164
[145] L. Wozniak. A Graphical Approach to Hydrogenerator Governor Tuning. IEEE Trans. on Energy Conversion, 1990, 5(3):417~421
[146] Karr, C. Genetic algorithm for fuzzy controllers. AI Expert, 1991, 233:435~448
[147] Yao X. Evolving artificial neural networks, IEEE Trans Neural Networks ,1999,87(9):1423~1447
[148] Ren LQ, Zhao ZY. An optimal neural network and concrete strength modeling. J Adv Engng Software, 2002, 33:117~130
[149] D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989, 24~46
[150] Hirose Y, Yamashita K, Hijiya S. Back-propagation algorithm which varies the number of hidden units. Neural Networks ,1991, 4:61~66
[151] F. Lampariello, M. Sciandrone. Efficient training of RBF neural networks for pattern recognition. IEEE Trans. Neural Networks, 2001, 12:1235~1242.
[152] Nicolaos B. Karayiannis, Yaohua Xiong. Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification. Transactions on neural Networks, September 2006, 17(5):1222~1235
[153] Z. Qiuming, C. Yao, L. Luzheng. A global learning algorithm for a RBF network. Neural Network, 1999, 12:527~540
[154] Faa-Jeng Lin, Rong-Jong Wai, Rou-Yong Duan. Fuzzy Neural Networks for Identification and Control of Ultrasonic Motor Drive with LLCC Resonant Technique. IEEE Trans. on Industrial Electronics, 1999, 46(5):999~1012
[155] Y.C. Chen, C.C. Teng. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems, 1995, 73:291~312
[156] T. Hasegawa, S. Horikawa, T. Furuhashi. On design of adaptive fuzzy controller using fuzzy neural networks and a description of its dynamical behavior. Fuzzy Sets and Systems, 1995, 71:3~23
[157] S.J. Horikawa, T. Furuhashi, Y. Uchikawa. On fuzzy modeling using fuzzy neural networks with the backpropagation algorithms. IEEE Trans. Neural Networks, 1992, 3:801~806
[158] Puyin Liu and Hongxing Li. Efficient Learning Algorithms for Three-Layer Regular Feedforward Fuzzy Neural Networks. IEEE Trans. on Neural Networks, 2004, 15(3):545~559
[159] Carvajal James, Chen Guanrong, Ogmen Haluk. Fuzzy PID controller: Design, performance evaluation, and stability analysis. Information Sciences, 2000, 123: 249~270
[160] Xu Jian-Xin, Hang Chang-Chieh, Liu Chen. Parallel structure and tuning of a fuzzy PID controller. Automatica, 2000, 36:673~684
[161] Ketata R., De Geest D., Titli. A. Fuzzy controller: design, evaluation, parallel and hierarchical combination with a PID controller. Fuzzy Sets and Systems, 1995, 71:113~129
[162] Wu ZhiQiao, Mizumoto, Masaharu. PID type fuzzy controller and parametersadaptive method. Fuzzy Sets and Systems, 1996, 78:23~35
[163] Mizumoto, M. Realization of PID controls by fuzzy control methods. Fuzzy Sets and Systems, 1995, 70:171~182
[164] Aduan Shaout, Jeff scharboceau. Fuzzy logic based modification system for the learning rate in back-propagation. Computers and Electrical Engineering, 2000, l26:125~129
[165] R. Collobert, Y. Bengio, and S. Bengio. Scaling large learning problems with hard parallel mixtures. Int. J. Pattern Recognit. Artif. Intell, 2003, 17(3):349~365
[166] Chih-Min Lin, Chun-Fei Hsu. Supervisory Recurrent Fuzzy Neural Network Control of Wing Rock for Slender Delta Wings. IEEE Trans. on Fuzzy Syztems, 2004, 12(5):733~743
[167] Yin Wang, Gang Rong. A self-organizing neural-network based fuzzy system. Fuzzy Sets and Systems, 1999, 103:1~11.
[168] Minghu Jiang, Georges Gielen, Beixing Deng. A fast learning algorithm for time-delay neural networks. Information Sciences, 2002, 148: 27~39
[169] Zhiming Zhang, Yue Wang, Ran Tao et al. An improved self-organizing CPN-based fuzzy system with adaptive back-propagation algorithm. Fuzzy Sets and Systems, 2002, 130:227~236
[170] Siu-yeung Cho, Tommy W.S. Chow. Training multilayer neural networks using fast global learning algorithm-least-squares and penalized optimization methods. Neurocomputing,1999, 25:115~131
[171] J. Xudong, H.K.S.W. Alvin. Constructing and training feed-forward neural networks for pattern classification. Pattern Recognition, 2002, 36:853~867
[172] Yunfei Zhou, Shuijin Li, Rencheng Jin. A new fuzzy neural network with fast learning algorithm and guaranteed stability for manufacturing process control. Fuzzy Sets and Systems, 2002, 132:201~216
[173] C.M. Cheng, N.W. Rees. Stability analysis of fuzzy multivariable systems: vector Lyapunov function approach. IEE Proc. Control Theory Appl. 1997, 144:403~412
[174] G. Feng, S.G. Cao, C.K. Chak. Design of fuzzy control systems with guaranteed stability, Fuzzy Sets and Sytems, 1997, 85:1~10
[175] Nan Jiang, Zhiye Zhao, Liqun Ren. Design of structural modular neural networks with genetic algorithm. Advances in Engineering Software, 2003, 3:17~24
[176] G.-B. Huang, Q.-Y. Zhu, K. Z. Mao, C.-K. Siew, P. Saratchandran, N. Sundararajan. Can threshold networks be trained directly. IEEE Trans. Circuits Syst. II, Mar. 2006, 53(3):187~191
[177] Bebis G, Georgiopoulos M, Kaspairs T. Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization. Neuro computing,1997,17:167~194
[178] Michalewicz Z. Genetic Algorithms + Data Structutre= Evolution Programs. Springer-Verlag, Second, Extended Edition, 1994, 18~31
[179] Winter G. Genetic Algorithms in Engineering and Computer Science. Wiley, 1995
[180] Sung-Kwun Oh, Witold Pedrycz. Genetic optimization-driven multi-layer hybrid fuzzy neural networks. Simulation Modelling Practice and Theory, 2005, 14: 597-613
[181] Blanco A, Delgado M,Pegalajar M C. A real-coded genetic algorithm for training recurrent neural networks. Neural networks, 2003, 14(1): 93~106
[182] Rak A, Aliev, Bijan Fazlollahi, Rustam M.Vahidov. Genetic algorithm-based learning of fuzzy neural networks. Part1: Feed-forward fuzzy neural networks. Fuzzy Sets and Systems, 2001, 118:351~358
[183] Ou Jinping. A gentic algorithm with real integer mixture encoding for optimization of fuzzy neural network control system. Earthquake engineering and engineering vibration, 2003, 25(1):12~17
[2] 叶鲁卿.水力发电过程控制—理论、应用及其发展.第一版.武汉:华中科技大学出版社,2002.7~333
[3] 李朝晖,郭江.调速器液压系统的故障诊断及可靠控制策略.水电能源科学,1999,17(3):40~44
[4] 李朝晖.微计算机控制系统可靠性理论研究及其在水轮机调节中的应用.[博士学位论文].武汉:华中科技大学图书馆,1991
[5] 魏守平,罗萍,张富强.水轮机调节系统的适应式变参数控制. 水电能源科学,2003,21(1):32~35
[6] Scott C. Bonnert, L. Wozniak. Adaptive Speed Control of Hydrogenerators by Recursive Least Squares Identification Algorithm. IEEE Trans. On Energy Conversion, March 1995, 10(1):162~168.
[7] 蔡维由,陈光大,刘炳文. 水轮机调速器的极点配置法设计及自适应控制. 大电机技术,1995,6:47~56
[8] Ye Luqing, Wei Shouping, Li Zhaohui et al. An Intelligent Self-Improving Control Strategy with a Variable Structure and Time-Varying Parameters for Water Turbine. La Houille Blanche, 1989, 6:463~475
[9] Ye Luqing. Variable Structure Control and Its Applications to Hydroelectric Generating Unit. Journal of Hydroelectric Engineering, 1992, 1:28~38
[10] Lansbeerry J E, Wozniak L. Optimal hydrogenerator governor tuning with a genetic algorithm. IEEE Trans on Energy Conversion, 1992, 7:623~628
[11] Jin Jiang. Design of an Optimal Robust Governor for Hydraulic Turbine Generating Units. IEEE Trans. on Energy Conversion, 1995, 10(1): 188~194
[12] O.P. Malik,Y. Zeng. Design of a Robust Adaptive Controller for a Water Turbine Governing System. IEEE Trans. on Energy Conversion, 1995, 10(2):354~359
[13] 程远楚,水电机组智能控制策略与调速励磁协调控制的研究.[博士学位论文].武汉:华中科技大学图书馆,2002
[14] 孙增沂.智能控制理论与技术.北京:清华大学出版社,第一版,1997.54~87
[15] 李人厚.智能控制理论和方法.西安:西安电子科技大学出版社,第一版,1999.23~76
[16] 蔡自兴.智能控制.北京:电子工业出版社,第二版,2004.12~56
[17] Panos J. Antsaklis. Intelligent Learning Control. IEEE Control System Magazine, 1995, 15(3):5~7
[18] Gellent Stephen I. Neural Network Learning and Expert System. Cambridge Massachusetts, London, England, The MIT Press, 1993,45~76
[19] Yuan Zeng-Ren, Guo Xin-Gang. Back-Propagation Neural Networks for the Inverse Control of Discrete-Time Nonlinear Plant. Advances in Modeling & Analysis, C, AMSE Press, 1994, 40(4):13~23
[20] Huang, S. H, ZHANG, H. C. Artificial Neural Networks in Manufacturing: Concepts, Applications and Perspectives. IEEE Transactions on Components, Packaging and Manufacturing Technology, Part A, 1994, 17(2): 212~228
[21] Filev D P, Yager R R. On the analysis of fuzzy logic controllers. Fuzzy Sets and Systems, 1994, 68:39~66
[22] Mu-Song Chen, Shinn-Wen Wang. Fuzzy clustering analysis for optimizing fuzzy membership function. Fuzzy Sets and Systems, 1999, 103:239~254
[23] F.Aminzadeh and M.Jamshidi. Soft Computing-Fuzzy Logic, Neural Networks, and Distributed Artificial Intelligence. Northern Ireland, Prentice-Hall, 1994,23~78
[24] L.A.Zadeh. Fuzzy Logic, Neural Networks, and Soft Computing. Comm. ACM, 1994, 37(3):77~84
[25] E.Mizutani, H.Takagi, and D.M.Auslander. A cooperative system based on soft computing methods to realize higher precision of computer color recipe prediction. In Proceedings of Application on OE/Arospace Sensing and Dual Use Photonics, 1995, 303~314
[26] 魏守平.水轮机控制工程.武汉:华中科技大学出版社,2005,13~28
[27] 方红庆,孙美凤,沈祖诒.水轮机调节系统控制策略综述.人民长江,2004,1:45~49
[28] 景雷.水轮机调节系统智能控制理论及应用研究.[博士学位论文].武汉:华中科技大学图书馆,1997.
[29] 沈祖诒.水轮机调节.北京:水利水电出版社.1998,35~58
[30] L. M. Hovey. Optimum adjustment of Hydro Governors on Manitoba Hydro System. AIEE Trans. Part III, 1962, 81:581~587
[31] L. D. Murphy. A Digital Governor for Hydrogenerators. IEEE Trans. on Energy Conversion, 1988, 3(4):780~784
[32] 魏守平,罗萍,张富强.水轮机调节系统的适应式变参数控制. 水电能源科学,2003,4:64~67
[33] 魏守平.水轮机调节系统的适应式变参数调节.大电机技术,1985,5:48~54
[34] 魏守平,卢本捷.水轮机调速器的 PID 调节规律.水力发电学报,2003, 4:112~118
[35] G. Orelind. Optimal PID Gain Schedule for Hydrogenerators—Design and Application. IEEE Trans. on Energy Conversion, 1989, 4(3):300~317
[36] Lansbeerry J E, Woznliak L. Adaptive hydrogenerator governor tuning with a genetic algorithm. IEEE Trans on Energy Conversion, 1994, 9(1):179~183
[37] O.P. Malik. An Intelligent Self-Improving Control Strategy with a Variable Structure and Time-Varying Parameters for Water Turbine. La Houille Blanche, 1989, 6:463~475
[38] Scott C. Bonnert, L. Wozniak. Adaptive Speed Control of Hydrogenerators by Recursive Least Squares Identification Algorithm. IEEE Trans. On Energy Conversion, 1995, 10(1):162~168
[39] 陈光大,刘炳文,蔡维由等.基于测试系统频率的自适应水轮机调速器.水力发电学报,1993,2:34~37
[40] Ye Luqing. Multivariable Adaptive Control and How to achieve it with a Microprocessor. Proceedings of the International Symposium on Applied Control and Identification, Copenhagen, Denmark, 1983, 20~25
[41] Ye Luqing. Multiprocessor-Based Fault Tolerant Adaptive Parameter-Optimized Governor for Water Turbine. Proceedings of Mini and Microcomputers and Their Applications, Saint Feliu, Spain, June 1985, 57~62
[42] Jing Lei, Ye Luqing. An Intelligent discontinuous control strategy for hydroelectric generating unit. IEEE Trans. on Energy Conversion, 1999, 13(1):84~89
[43] 王柏林.水轮发电机组的模型参考自适应控制.自动化学报,1987,13(6): 408~415
[44] 蔡维由,陈光大,刘炳文.水轮机调速器的极点配置法设计及自适应控制.大电机技术,1995,6:78~81
[45] 孟佐宏,蔡维由,陈光大等.水轮机调节系统最优鲁棒极点配置调速器的设计.大电机技术,2000,4:32~35
[46] Jin Jiang. Design of an Optimal Robust Governor for Hydraulic Turbine Generating Units. IEEE Trans. on Energy Conversion, 1995, 10(1):188~194.
[47] 余刃.智能控制-维护-管理集成系统框架下预知维护理论与应用.[博士学位论文]. 武汉:华中科技大学图书馆,2000
[48] Ye Luqing. Variable Structure Control and Its Applications to Hydroelectric Generating Unit. Journal of Hydroelectric Engineering, 1992, 1:28~38
[49] Ye Luqing. Variable Structure and Time-Varying Parameter Control for Hydroelectric Generating Unit. IEEE Trans. on Energy Conversion, 1989, 4(3):293~299
[50] Ye Luqing. An Intelligent Self-Improving Control Strategy with a Variable Structure and Time-Varying Parameters for Water Turbine. La Houille Blanche, 1989, 6:463~475
[51] Louis Wozniak, Daniel J. Bitz. Load-Lever-Sensitive Governor for Speed Control of Hydrogenerators. IEEE Trans. on Energy Conversion, 1988, 3(1):78~84
[52] 金和平.多变量预测控制及其在水电生产过程中的应用研究.[博士学位论文] .武汉:华中理工大学图书馆,1993.
[53] 刘欣,尹绍清,肖顺达.模糊自适应 Smith 预估控制及其应用.控制理论与应用.1993,73:225~228
[54] 陈启卷.系统辨识和智能化控制及在水电机组中的应用.[博士学位论文].武汉:武汉大学图书馆,1999.
[55] M. Lown, E. Swidenbank, B. W. Hogg. Adaptive Fuzzy Logic Control of a Turbine Generator System. IEEE Trans. on Energy Conversion, 1997, 12(4):394~399
[56] Braae M, Rutherford D A. Theoretical and Linguistic Aspects of Fuzzy Logic Controller. Automatica, 1979, 15(5):553~577
[57] J. J. Buckly. An Fuzzy Expert Systems. Fuzzy Sets and Systems, August 1986, 20(1):1~16
[58] 龚崇权,肖惠民,蔡维由. 模糊控制在水轮机调节应用中运行模式的探讨. 长沙电力学院学报(自然科学版),2001,16(3):52~54
[59] 林富华,沈恩源,林建亚等. 模糊 PID 技术在水轮机调节系统中的应用. 动力工程,1996,2:45~48.
[60] Daijin Kim. A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation. Fuzzy Set and Systems, 2002, 125:93~104
[61] Zhang Y., Chen G.P., Malik O.P., Hope G.S. An Artificial Neural Network based Adaptive Power System Stabilizer, IEEE Transaction on Energy Conversion, 1993, 8(1):71~77
[62] Djukanovic, M.B, Calovic, M.S,Vesovic, B.V. Neuro-fuzzy Controller of Low Head Hydropower Plants using Adaptive-network based Fuzzy Inference System, IEEE Transaction on Energy Conversion, 12(4), 1997: 375 ~381.
[63] 郭创新,梁年生,叶鲁卿.基于神经网络实现水轮机自学习 PID 调节.水力发电学报,1997,1:36~39
[64] 景雷,叶鲁卿,周泰经. 基于在线学习的水轮发电机组智能控制系统研究.电力系统自动化,1997,4:43~47
[65] 蔡维由,刘海锋,陈光大等. 水轮机调节系统的模糊神经控制.长江科学院院报,2003,20(2):45~49
[66] 龚崇权. 基于遗传算法的水轮机调节系统研究.[博士学位论文].武汉:武汉大学图书馆,2002
[67] 刘乐星,毛宗源.水轮机的 GA-PID 控制器研究.电力系统自动化,1997,12:65~69
[68] 孟安波, 叶鲁卿, 殷豪.遗传算法在水电机组调速器 PID 参数优化中的应用.控制理论与应用,2004,21(3):398~407
[69] 南海鹏,罗兴绮,余向阳.基于遗传算法的水轮机智 PID 调速器研究. 水力发电学报,2004,23(2):107~112
[70] 龚崇权,蔡维由,肖惠民.基于遗传算法的水轮机调节系统最优参数整定.电力系统自动化,2002,26(15):57~59
[71] 廖忠,沈祖诒. 基于正交交叉操作的遗传算法及在水轮机调速器参数优化中的应用.大电机技术,2003,5:61~64
[72] 余向阳,南海鹏,杨晓萍. 基于遗传算法的水轮机模糊自适应调速器研究. 大电机技术,2004,1:63~67
[73] Ra_k A. Aliev, Bijan Fazlollahi, Rustam M. Vahidov. Genetic algorithm-based learning of fuzzy neural networks. Part 1: Feed-forward fuzzy neural networks, Fuzzy Sets and Systems, 2001, 118:351~358
[74] Cheng-Jian Lin. A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems, 2004, 143:311~333
[75] Zadeh L.A. From circuit theory to system theory. Proc.IRE, 1962, 50(5):856~865
[76] Chin-Teng Lin. Neural Network-Based Fuzzy Logic Control and Design System, IEEE Trans. on Computer, 40(12), Dec. 1991: 1320~1336.
[77] Ljung L. Convergence analysis of parametric identification methods. IEEE Trans on Automatic Control, 1978, 23:770~783
[78] 方崇智,萧德云.过程辨识.北京:清华大学出版社,1988,34~46
[79] 黄正良,吴坚,万百五. 辨识 Wiener 模型的一种新方法.控制理论与应用.1996, 13 (3):326-332
[80] Billings S.A, Fakhovri S. V. Nonlinear System Identification Using the Hammerstein Model. Int.J, System Science, 1979, 10(5):23~39
[81] 孙昀,沈祖诒.基于模糊神经网络的水力机组模型辨识.河海大学学报,2000,2:32~35
[82] Rainer Palm, Robust Control by Fuzzy Sliding Mode, Automatica, 30(9), 1994:1429~1434
[83] Leonlaritis I.J, Billings S.A. Input-output parametric models for nonlinear systems Part II: stochastic nonlinear systems. Int.J.Control,1985,41(2):329~334
[84] Chen S, Billings S.A. Representations of nonlinear systems: the NARMAX model.Int. J. control, 1989, 49(3):1013~1032
[85] Billings S.A, Chen S. Extended model set, global data and threshold model identification of severely nonlinear systems. Int.J.Control, 1989, 50:1897~1892
[86] Chen S, Billings S.A. Neural network for nonlinear dynamic system modeling and identification .Int.J.control,1992, 5: 319~346
[87] Chen S, Billings S.A, Grant P. Nonlinear system identification using neural network Int.J. control, 1990, 51:1191~1214
[88] Narendra K.S, Parthasarathy L. Identification and Control of Dynamical Systems Using Neural Net works. IEEE Trans On Neural Networks,1990,1(1):4~27
[89] 刘延年,冯纯伯. 用神经网络进行非线性离散动态系统辨识的可行性. 控制理论与应用,1994,11(4):413~420
[90] 任雪梅,高为炳.基于神经网络非线性系统辨识和控制的研究.控制理论与应用, 1995,12(2):147~153
[91] 鲍晓红,贾英民.用动态隐层的前馈网辨识非线性系统.自动化学报.1997,23(5):689~693.
[92] 王淑青,李朝晖. 基于自适应模糊神经网络的水轮机特性辨识研究.武汉大学学报,2006,2:74~76
[93] Fukuda Toshio, Ghibata Takanori. Theory and Application of Neural Networks for Industrial Control Systems. IEEE Transaction on Industrial Electronics, December 1992,39(6):472~489
[94] 程远楚,叶鲁卿,蔡维由.水轮机特性的神经网络建模. 华中科技大学学报, 2003,6:68~70
[95] 常江,谢云敏.混流式水轮机神经网络模型非线性仿真.中国农村水利水电, 2004,6:75~77
[96] Jiang Chang, Yan Peng. Movable Propeller Turbine Neural Network Model and Nonlinear Simulation. In: Institute of Electrical and Electronics Engineers Inc, The Fourth International Conference on Machine Learning and Cybernetics. Baoding. Hebei Univeristy. 2005, 1220~1225.
[97] 郭君,董朝霞.基于神经网络的水轮发电机组的建模分析.电力系统及其自动化学报,2003,12:37~41
[98] Vila, M.A., Cubero, J.C., Medina, J.M., Pons, O. Soft computing: a new perspectivefor some data mining problems. Vistas in Astronomy, 1997, 41(3):379~386
[99] Arnold F. Shapiro. The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance: Mathematics and Economics, August, 2002, 131:115~131
[100] 权太范.模糊控制技术在控制中的应用现状及前景.控制与决策,1988,3(1):59~62
[101] A. MUHAMMAD, BARGIELA. Fuzzy and Evolutionary Modelling of Nonlinear Control Systems. Mathematical and Computer Modelling, 2001, 33: 533-551
[102] Linkens D A, Nie J. Constructing rule-bases for multivariable fuzzy control by self-learning, PartⅠ : System structure and learning algorithms. Int J Systems Sci, 1993, 24(1):111~127
[103] Ronald R. Yager. Implementing fuzzy logic controllers using a neural network framework, Fuzzy Sets and Systems, 1999, 100:133~144
[104] Chen C L., Chen W C. Self-organizing Neural Control Systems Design for Dynamic Process. International Journal of System Science, 1993, 24(8):1487~1507
[105] L.A. Zadeh. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Computing, 1998, 2:23~25
[106] L A. Zadeh. Fuzzy Set. Information and control, 1965, 8(2):338~358
[107] L.A. Zadeh. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics,1973, 3:28~44
[108] Tobi T. The application of fuzzy control to a coke oven gas cooling plant. Fuzzy Sets and Systems, 1992, 46:373~381
[109] Mamdani E H. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc IEEE, 1974,121:1585~1588
[110] Gupta M M. Multivariable structure of fuzzy control systems. IEEE Trans Syst, Man, Cybern, 1985, 15(2):260~271
[111] Gegov A. Multilevel intelligent fuzzy logic control of oversaturated urban traffic networks. Int J Systems Sci,1994, 25(6):967~978
[112] Khee F V D, Linkens D A, Nie J. Constructing rule-bases for multivariable fuzzy control by self-learning, Part Ⅱ: Rule-base formation and blood pressure control application. Int J Systems Sci, 1993, 24(1):129~157
[113] E.B.Baum and F. Wilczek. Supervised learning of probability distribution by neural networks. Neural information processing systems, 1988, 23:52-61
[114] Zhang Huaguang, Li Longcai, Bien Zeungnam. A multivariable generalized predicative control approach based on T-S fuzzy model. Int Journal of Intelligent & Fuzzy Systems, 2000, 9(3):169~190
[115] H. A. E de Bruin, B.Roffel. A new identification method for fuzzy linear models of nonlinear dynamic systems, Journal of Process Control, 1996, 6:277~293
[116] K.J. Hunt, D. Sbarbaro, R. Zbikowski, P.J. Gawthrop. Neural networks for control systems-a survey, Automatica, 1992, 28:1083~1112
[117] J.-S. Roger Jang. Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In Proceedings of the ninth National Conference on Artificial Intelligence(AAAI-91), 1991, 762-767
[118] J.Moody,C.Darken. Fast Learning in Network of Locally-tuned Processing Units. Neural Computation, 1989,1(2):281~294
[119] J.J.Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, USA, 1982, 79:2554~2558
[120] Zurada. Jacek M. Introduction to Artificial Neural Systems, New York: West Publishing Company. 1992, 59~74
[121] Simpson Patrick K. Artificial Neural Systems Foundation, Paradigms, Applications and Implementations. Inc. APPENDIX. Pergamon Press, 1990
[122] Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation in parallel distributed processing. Rumelhart D e and McClelland J L, Eds. Cambridge, MA: MIT Press, 1986, 1:318~360
[123] D.S. Brommhead and D.Lowe. Multivariate functional interpolation and adaptive networks, Complex Systems, 1988, 2:321~355
[124] Min Han, Jianhui Xi. Efficient clustering of radial basis perception neural network for pattern recognition. Pattern Recognition. 2004, 37:2059~2067
[125] Caizhong Tian, Takao Fujii. Nonlinear system identification of rapid thermal processing. Control Engineering Practice, 2005, 13:681~687
[126] Ahmed Rubaai, Raj Kotaru. Adaptation Learning Control Scheme for a High-Performance Permanent-Magnet Stepper Motor Using Online Random Training of Neural Networks, IEEE Transactions on Industry Applications, 2001, 37(2):495~504
[127] Shuzhi Sam Ge, Jin Zhang, Tong Heng Lee. Adaptive Neural Network Control for a Class of MIMO Nonlinear Systems with Disturbances in Discrete-Time. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, August 2004, 34(4):1630~1645
[128] K.J.Hunt, D Sbarbao. Neural Networks for Nonlinear Internal Model Control. IEE Proc.-D,1991, 139(5):431~438
[129] M.M. Awais. Application of internal model control methods to industrial combustion. Applied Soft Computing , 2005, 5:223~233
[130] Flanagan J. Randall, Vetter Philipp, Johansson, Roland S. Prediction PrecedesControl in Motor. Learning Current Biology, January 2003, 13:146~150
[131] Cheng-Jian Lin. A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems, 2004, 143:311~333
[132] Rafik A. Aliev, Bijan Fazlollahi, Rustam M. Vahidov. Genetic algorithm-based learning of fuzzy neural networks. Part 1:feed-forward fuzzy neural networks, Fuzzy Sets and Systems, 2001, 118:351~358
[133] Yie-Chien Chen, Ching-Cheng Teng. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems, 1995, 73:291~312
[134] L. Jiexing, Z. Yun, F. Xi. An improved closest cluster learning algorithm. Control Theory Appl. 2000, 17:735~738
[135] Y. Hayashi, J. Buckley, E. Czogola. Fuzzy neural networks with fuzzy signals and weights. Internat. J. Intell. Systems, 1993, 8:527~537
[136] M.Srinivas and L.M.Patnaik. Genetic algorithms: A survey. IEEE Computer, June 1994, 17~26
[137] Bala J. Shape Analgsis using Hybird Learning. Pattern Recognition,1996, 29(8):1323~1333
[138] K.A.De Jong. Learning with Genetic Algorithm: An Overview. Machine Learning, March 1988, 3:121~138
[139] Kuncheva L I. Fitness Functions in Editing K-NN Reference Set by Genetic Algorithms. Pattion,1997,30(6): 1041~1049
[140] Bhanu B, Lee S, Ming J. Self-Optimizing Image Segmentation System Using a Genetic Algorithm. In: Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, 1991, 362~369
[141] Beasley D, Bull D R. A Sequencing Niche Technique for Multimodal Function Optomization. Evolutionary Computation, 1993, 1(2):101~125
[142] Koji Shimojima. Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm. Fuzzy Sets and Systems, 1995, 71:295~309
[143] Montana,D.J. Automated parameter tuning for interpretation of synthetic images. Handbook of genetic algorithms, VNR Computer Libiary,1989
[144] Tien-Chi Chen, Tsong-Terng Sheu. Model Reference Neural Network Controller for Induction Motor Speed Control. IEEE Tran. on Energy Conversion, 2002, 17(2):157~164
[145] L. Wozniak. A Graphical Approach to Hydrogenerator Governor Tuning. IEEE Trans. on Energy Conversion, 1990, 5(3):417~421
[146] Karr, C. Genetic algorithm for fuzzy controllers. AI Expert, 1991, 233:435~448
[147] Yao X. Evolving artificial neural networks, IEEE Trans Neural Networks ,1999,87(9):1423~1447
[148] Ren LQ, Zhao ZY. An optimal neural network and concrete strength modeling. J Adv Engng Software, 2002, 33:117~130
[149] D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989, 24~46
[150] Hirose Y, Yamashita K, Hijiya S. Back-propagation algorithm which varies the number of hidden units. Neural Networks ,1991, 4:61~66
[151] F. Lampariello, M. Sciandrone. Efficient training of RBF neural networks for pattern recognition. IEEE Trans. Neural Networks, 2001, 12:1235~1242.
[152] Nicolaos B. Karayiannis, Yaohua Xiong. Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification. Transactions on neural Networks, September 2006, 17(5):1222~1235
[153] Z. Qiuming, C. Yao, L. Luzheng. A global learning algorithm for a RBF network. Neural Network, 1999, 12:527~540
[154] Faa-Jeng Lin, Rong-Jong Wai, Rou-Yong Duan. Fuzzy Neural Networks for Identification and Control of Ultrasonic Motor Drive with LLCC Resonant Technique. IEEE Trans. on Industrial Electronics, 1999, 46(5):999~1012
[155] Y.C. Chen, C.C. Teng. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems, 1995, 73:291~312
[156] T. Hasegawa, S. Horikawa, T. Furuhashi. On design of adaptive fuzzy controller using fuzzy neural networks and a description of its dynamical behavior. Fuzzy Sets and Systems, 1995, 71:3~23
[157] S.J. Horikawa, T. Furuhashi, Y. Uchikawa. On fuzzy modeling using fuzzy neural networks with the backpropagation algorithms. IEEE Trans. Neural Networks, 1992, 3:801~806
[158] Puyin Liu and Hongxing Li. Efficient Learning Algorithms for Three-Layer Regular Feedforward Fuzzy Neural Networks. IEEE Trans. on Neural Networks, 2004, 15(3):545~559
[159] Carvajal James, Chen Guanrong, Ogmen Haluk. Fuzzy PID controller: Design, performance evaluation, and stability analysis. Information Sciences, 2000, 123: 249~270
[160] Xu Jian-Xin, Hang Chang-Chieh, Liu Chen. Parallel structure and tuning of a fuzzy PID controller. Automatica, 2000, 36:673~684
[161] Ketata R., De Geest D., Titli. A. Fuzzy controller: design, evaluation, parallel and hierarchical combination with a PID controller. Fuzzy Sets and Systems, 1995, 71:113~129
[162] Wu ZhiQiao, Mizumoto, Masaharu. PID type fuzzy controller and parametersadaptive method. Fuzzy Sets and Systems, 1996, 78:23~35
[163] Mizumoto, M. Realization of PID controls by fuzzy control methods. Fuzzy Sets and Systems, 1995, 70:171~182
[164] Aduan Shaout, Jeff scharboceau. Fuzzy logic based modification system for the learning rate in back-propagation. Computers and Electrical Engineering, 2000, l26:125~129
[165] R. Collobert, Y. Bengio, and S. Bengio. Scaling large learning problems with hard parallel mixtures. Int. J. Pattern Recognit. Artif. Intell, 2003, 17(3):349~365
[166] Chih-Min Lin, Chun-Fei Hsu. Supervisory Recurrent Fuzzy Neural Network Control of Wing Rock for Slender Delta Wings. IEEE Trans. on Fuzzy Syztems, 2004, 12(5):733~743
[167] Yin Wang, Gang Rong. A self-organizing neural-network based fuzzy system. Fuzzy Sets and Systems, 1999, 103:1~11.
[168] Minghu Jiang, Georges Gielen, Beixing Deng. A fast learning algorithm for time-delay neural networks. Information Sciences, 2002, 148: 27~39
[169] Zhiming Zhang, Yue Wang, Ran Tao et al. An improved self-organizing CPN-based fuzzy system with adaptive back-propagation algorithm. Fuzzy Sets and Systems, 2002, 130:227~236
[170] Siu-yeung Cho, Tommy W.S. Chow. Training multilayer neural networks using fast global learning algorithm-least-squares and penalized optimization methods. Neurocomputing,1999, 25:115~131
[171] J. Xudong, H.K.S.W. Alvin. Constructing and training feed-forward neural networks for pattern classification. Pattern Recognition, 2002, 36:853~867
[172] Yunfei Zhou, Shuijin Li, Rencheng Jin. A new fuzzy neural network with fast learning algorithm and guaranteed stability for manufacturing process control. Fuzzy Sets and Systems, 2002, 132:201~216
[173] C.M. Cheng, N.W. Rees. Stability analysis of fuzzy multivariable systems: vector Lyapunov function approach. IEE Proc. Control Theory Appl. 1997, 144:403~412
[174] G. Feng, S.G. Cao, C.K. Chak. Design of fuzzy control systems with guaranteed stability, Fuzzy Sets and Sytems, 1997, 85:1~10
[175] Nan Jiang, Zhiye Zhao, Liqun Ren. Design of structural modular neural networks with genetic algorithm. Advances in Engineering Software, 2003, 3:17~24
[176] G.-B. Huang, Q.-Y. Zhu, K. Z. Mao, C.-K. Siew, P. Saratchandran, N. Sundararajan. Can threshold networks be trained directly. IEEE Trans. Circuits Syst. II, Mar. 2006, 53(3):187~191
[177] Bebis G, Georgiopoulos M, Kaspairs T. Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization. Neuro computing,1997,17:167~194
[178] Michalewicz Z. Genetic Algorithms + Data Structutre= Evolution Programs. Springer-Verlag, Second, Extended Edition, 1994, 18~31
[179] Winter G. Genetic Algorithms in Engineering and Computer Science. Wiley, 1995
[180] Sung-Kwun Oh, Witold Pedrycz. Genetic optimization-driven multi-layer hybrid fuzzy neural networks. Simulation Modelling Practice and Theory, 2005, 14: 597-613
[181] Blanco A, Delgado M,Pegalajar M C. A real-coded genetic algorithm for training recurrent neural networks. Neural networks, 2003, 14(1): 93~106
[182] Rak A, Aliev, Bijan Fazlollahi, Rustam M.Vahidov. Genetic algorithm-based learning of fuzzy neural networks. Part1: Feed-forward fuzzy neural networks. Fuzzy Sets and Systems, 2001, 118:351~358
[183] Ou Jinping. A gentic algorithm with real integer mixture encoding for optimization of fuzzy neural network control system. Earthquake engineering and engineering vibration, 2003, 25(1):12~17