基于智能计算的非线性系统辨识算法研究及其应用
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摘要
几乎所有的生产系统都是非线性系统,人们常说的线性系统是对系统非线性特性在某种程度上进行忽略或者某种假设条件下近似得到的,这种近似必然会产生误差,影响生产系统的控制效果。生产系统结构越来越复杂,包含的非线性特性也更多样化,简单的线性近似已经不能满足提高系统生产力的要求,所以,非线性系统辨识是大势所趋。到目前为止,没有一种通用的方法可以对不同结构的非线性系统进行辨识,一般是具有不同非线性特性的系统,用不同的辨识方法。
模块化的非线性模型结构简单,内部连接方式明了,比较适合生产系统的辨识,是近年来颇受研究者青睐的一种非线性系统辨识常用的模型。模块化模型主要有Hammerstein模型(H模型)、Wiener模型(W模型)及后来出现的Hammerstein-Wiener模型(H-W模型)和Wiener-Hammerstein模型(W-H模型)。
热工系统是规模庞大、结构复杂、控制要求较高的生产系统,生产过程中存在着不同程度的非线性,本文从自动控制系统的构成出发,分析了热工控制系统中执行器和检测变送器的非线性特性,进而得知,模块化的非线性模型适合于热工系统典型过程的辨识。
本文重点研究了三种模块化非线性模型：Hammerstein模型、Wiener模型以及Hammerstein-Wiener模型。采用粒子群算法及其改进算法优化模型参数,用神经网络理论构造新的模块化模型及推导模型自身学习规则。借助分散控制系统存储的热工过程输入输出数据,将模块化模型的辨识方法应用于热工系统的辨识中。本文主要内容包括：
1.提出了一种基于聚类分析的样条函数多项式Hammerstein模型,用粒子群算法寻优模型参数。将该辨识算法应用于热工系统某生产过程的辨识中,仿真结果表明了该样条函数Hammerstein模型辨识算法的有效性,为热工系统辨识提供了一种有效途径。
2.提出了两种网络化Wiener模型,两种模型分别用BP网络和RBF网络表示模型的非线性部分,将模型转换成串联的网络结构；两种模型都采用双层优化策略,用BP算法和粒子群算法分内外两层优化模型参数。将这两种方法应用于热工系统两个对象的辨识,CO2浓度系统的辨识结果表明网络化W模型较样条函数H模型效果好,主汽压系统的辨识结果表明网络化W模型有较好的适用性。
3.引入量子计算理论,用量子粒子群算法辨识一般指数多项式Hammerstein模型,并将该算法应用于热工系统的辨识。一般指数多项式模型结构简单,计算速度较快；量子粒子群算法较普通粒子群算法,增加了种群的多样性,一定程度上避免了早熟。从循环流化床机组三个对象的辨识结果可以看出,简单多项式H模型可以用于一部分实际系统的辨识,量子粒子群算法一定程度上可以提高指数多项式H模型的辨识精度。
4.提出了一种网络化Hammerstein-Wiener模型,研究了一般多项式H-W模型和文中提出的网络化H-W模型的辨识方法,用量子粒子群算法辨识模型参数。分别将两种模型应用于热工系统两个典型环节的辨识,仿真结果表明了H-W模型能较好地表达生产系统的特性。
本文主要研究基于智能计算的非线性模型辨识算法及其在热工系统中的应用,希望本文的研究工作能对热工系统的辨识有一定的理论与实践价值,并能对其它生产系统的辨识起到一定的启发作用。
Almost all production systems are nonlinear systems. The so-called linear system is obtained approximately under some assumptions or by ignoring to some nonlinear characteristics of the system, while this kind of approximation will inevitably produce errors, which will influence the control effect of production systems. As the structure of production system becomes more and more complex, nonlinear characteristics contained in those systems are also more diverse, as a result, simple linear approximation can no longer meet the requirement of improving system productivity, and therefore, nonlinear system identification has become the general trend. So far, there is no universal method to identify nonlinear systems with different structures, usually, different methods to differernt systems with different nonlinear characteristics.
The modular nonlinear model has not simple structure only, but also clear internal connections, so it is popular and widely used by researchers in recent years. Modular models mainly include Hammerstein model (H model), Wiener model (W model), and the succeeding Hammerstein-Wiener model (H-W model) and Wiener-Hammerstein model (W-H model).
Thermal system is large-scale production system with complex structure and high demand for control performance, and there are different degrees of nonlinearity in the production process. In this thesis, analyzes nonlinear characteristics of actuators and detection transmitters in thermal control systems, starting from the composition of the automatic control system, and then it is learned that modular nonlinear models are suitable for the identification of typical processes of thermal systems.
Three modular nonlinear models are mainly studied in this thesis:Hammerstein model, Wiener model, and Hammerstein-Wiener model. Model parameters are optimized using particle swarm optimization algorithm and its improved algorithm. With neural network theory, new modular models are constructed and the learning rules of the models itself are deduced. Identification methods of modular models are applied to the identification of thermal systems, with the input and output data of thermal processes stored in distributed control systems. The main contents include:
1. Spline function polynomial Hammerstein model based on cluster analysis is introduced and particle swarm optimization algorithm is used to optimize model parameters. This identification algorithm is applied to identify a typical link of thermal system, and the simulation results show the effectiveness of this spline function Hammerstein model, which provided an effective way for identification of production systems.
2. Two networked Wiener models are introduced, the nonlinear parts of which are represented by BP network and RBF network respectively, and then the models can be converted into series structures. Both of these two kinds of models adopt double-optimization strategy, which optimizes networked models in inner and outer layers with BP algorithm and particle swarm optimization algorithm respectively. These two methods are applied to identification of two objects of thermal systems. The identification results of CO2concentration system show that networked W model outperforms spline function H model, and the identification results of main-steam pressure system show that networked W model has better applicability.
3. Identify general index polynomial Hammerstein model with quantum particle swarm optimization (QPSO) algorithm, by adopting quantum computing theory, and apply this algorithm to the identification of thermal systems. Generally index polynomial H model has simple structure and faster calculation speed. Precocity is avoided to some extent, for the diversity of population is increased in QPSO algorithm, compared with general particle swarm optimization (PSO) algorithm. From the identification results of three objects of circulating fluidized bed unit, it can be seen that simple polynomial H model can be used for part of the actual system identification and quantum particle swarm algorithm can improve the identification accuracy of model to some extent.
4. One kind of networked Hammerstein-Wiener model is introduced, and two identification methods of general polynomial H-W model and the networked model proposed in this paper are studied, adopting QPSO algorithm to identify the parameters of the models. The two methods are applied to the identification of two typical production links in thermal systems, and simulation results show that H-W models are more capable to express characteristics of production systems.
This thesis studies mainly the identification algorithms of nonlinear models and its application in thermal systems based on intelligent computing. It's wished that the research work in this thesis has some theoretical and practical value for the identification of thermal systems and some enlightening action for the identification of other production systems.
模块化的非线性模型结构简单,内部连接方式明了,比较适合生产系统的辨识,是近年来颇受研究者青睐的一种非线性系统辨识常用的模型。模块化模型主要有Hammerstein模型(H模型)、Wiener模型(W模型)及后来出现的Hammerstein-Wiener模型(H-W模型)和Wiener-Hammerstein模型(W-H模型)。
热工系统是规模庞大、结构复杂、控制要求较高的生产系统,生产过程中存在着不同程度的非线性,本文从自动控制系统的构成出发,分析了热工控制系统中执行器和检测变送器的非线性特性,进而得知,模块化的非线性模型适合于热工系统典型过程的辨识。
本文重点研究了三种模块化非线性模型：Hammerstein模型、Wiener模型以及Hammerstein-Wiener模型。采用粒子群算法及其改进算法优化模型参数,用神经网络理论构造新的模块化模型及推导模型自身学习规则。借助分散控制系统存储的热工过程输入输出数据,将模块化模型的辨识方法应用于热工系统的辨识中。本文主要内容包括：
1.提出了一种基于聚类分析的样条函数多项式Hammerstein模型,用粒子群算法寻优模型参数。将该辨识算法应用于热工系统某生产过程的辨识中,仿真结果表明了该样条函数Hammerstein模型辨识算法的有效性,为热工系统辨识提供了一种有效途径。
2.提出了两种网络化Wiener模型,两种模型分别用BP网络和RBF网络表示模型的非线性部分,将模型转换成串联的网络结构；两种模型都采用双层优化策略,用BP算法和粒子群算法分内外两层优化模型参数。将这两种方法应用于热工系统两个对象的辨识,CO2浓度系统的辨识结果表明网络化W模型较样条函数H模型效果好,主汽压系统的辨识结果表明网络化W模型有较好的适用性。
3.引入量子计算理论,用量子粒子群算法辨识一般指数多项式Hammerstein模型,并将该算法应用于热工系统的辨识。一般指数多项式模型结构简单,计算速度较快；量子粒子群算法较普通粒子群算法,增加了种群的多样性,一定程度上避免了早熟。从循环流化床机组三个对象的辨识结果可以看出,简单多项式H模型可以用于一部分实际系统的辨识,量子粒子群算法一定程度上可以提高指数多项式H模型的辨识精度。
4.提出了一种网络化Hammerstein-Wiener模型,研究了一般多项式H-W模型和文中提出的网络化H-W模型的辨识方法,用量子粒子群算法辨识模型参数。分别将两种模型应用于热工系统两个典型环节的辨识,仿真结果表明了H-W模型能较好地表达生产系统的特性。
本文主要研究基于智能计算的非线性模型辨识算法及其在热工系统中的应用,希望本文的研究工作能对热工系统的辨识有一定的理论与实践价值,并能对其它生产系统的辨识起到一定的启发作用。
Almost all production systems are nonlinear systems. The so-called linear system is obtained approximately under some assumptions or by ignoring to some nonlinear characteristics of the system, while this kind of approximation will inevitably produce errors, which will influence the control effect of production systems. As the structure of production system becomes more and more complex, nonlinear characteristics contained in those systems are also more diverse, as a result, simple linear approximation can no longer meet the requirement of improving system productivity, and therefore, nonlinear system identification has become the general trend. So far, there is no universal method to identify nonlinear systems with different structures, usually, different methods to differernt systems with different nonlinear characteristics.
The modular nonlinear model has not simple structure only, but also clear internal connections, so it is popular and widely used by researchers in recent years. Modular models mainly include Hammerstein model (H model), Wiener model (W model), and the succeeding Hammerstein-Wiener model (H-W model) and Wiener-Hammerstein model (W-H model).
Thermal system is large-scale production system with complex structure and high demand for control performance, and there are different degrees of nonlinearity in the production process. In this thesis, analyzes nonlinear characteristics of actuators and detection transmitters in thermal control systems, starting from the composition of the automatic control system, and then it is learned that modular nonlinear models are suitable for the identification of typical processes of thermal systems.
Three modular nonlinear models are mainly studied in this thesis:Hammerstein model, Wiener model, and Hammerstein-Wiener model. Model parameters are optimized using particle swarm optimization algorithm and its improved algorithm. With neural network theory, new modular models are constructed and the learning rules of the models itself are deduced. Identification methods of modular models are applied to the identification of thermal systems, with the input and output data of thermal processes stored in distributed control systems. The main contents include:
1. Spline function polynomial Hammerstein model based on cluster analysis is introduced and particle swarm optimization algorithm is used to optimize model parameters. This identification algorithm is applied to identify a typical link of thermal system, and the simulation results show the effectiveness of this spline function Hammerstein model, which provided an effective way for identification of production systems.
2. Two networked Wiener models are introduced, the nonlinear parts of which are represented by BP network and RBF network respectively, and then the models can be converted into series structures. Both of these two kinds of models adopt double-optimization strategy, which optimizes networked models in inner and outer layers with BP algorithm and particle swarm optimization algorithm respectively. These two methods are applied to identification of two objects of thermal systems. The identification results of CO2concentration system show that networked W model outperforms spline function H model, and the identification results of main-steam pressure system show that networked W model has better applicability.
3. Identify general index polynomial Hammerstein model with quantum particle swarm optimization (QPSO) algorithm, by adopting quantum computing theory, and apply this algorithm to the identification of thermal systems. Generally index polynomial H model has simple structure and faster calculation speed. Precocity is avoided to some extent, for the diversity of population is increased in QPSO algorithm, compared with general particle swarm optimization (PSO) algorithm. From the identification results of three objects of circulating fluidized bed unit, it can be seen that simple polynomial H model can be used for part of the actual system identification and quantum particle swarm algorithm can improve the identification accuracy of model to some extent.
4. One kind of networked Hammerstein-Wiener model is introduced, and two identification methods of general polynomial H-W model and the networked model proposed in this paper are studied, adopting QPSO algorithm to identify the parameters of the models. The two methods are applied to the identification of two typical production links in thermal systems, and simulation results show that H-W models are more capable to express characteristics of production systems.
This thesis studies mainly the identification algorithms of nonlinear models and its application in thermal systems based on intelligent computing. It's wished that the research work in this thesis has some theoretical and practical value for the identification of thermal systems and some enlightening action for the identification of other production systems.
引文
[1]叶建华.过程辨识技术[M].上海：上海大学出版社,2007.
[2]方崇智,萧德云.过程辨识[M].北京：清华大学出版社,1988.
[3]冯恩民,修志龙.非线性发酵动力系统：辨识、控制与并行优化[M].北京：科学出版社,2012.
[4]吴立成,杨国胜,郐新凯,等.柔性臂机器人：建模、分析与控制[M].北京：高等教育出版社,2012.
[5]韩建达,何玉庆,赵新刚.移动机器人系统——建模、估计与控制[M].北京：科学出版社,2011.
[6]李洪心.可计算的—般均衡模型——建模与仿真[M].北京：机械工业出版社,2008.
[7]庞中红,崔红.系统辨识与自适应控制MATLAB仿真[M].北京：北京航空航天大学出版社,2013.
[8]刘金琨,沈晓蓉,赵龙.系统辨识理论及MATLAB仿真[M].北京：电子工业出版社,2013.
[9]曾凯文,文劲宇,程时杰,等.复杂电网连锁故障下的关键线路辨识[J].中国电机工程学报.2014,34(7)：1103-1112.
[10]贾杰,陈晨,曹姣,等.广义输出误差模型的两阶段最小二乘递推辨识[J].控制理论与应用,2014,31(2)：195-200.
[11]王建宏.丢失数据下的条件极大似然辨识[J].控制与决策,2014,29(2)：358-362.
[12]于丰,毛志忠,贾明兴,等.一种Hammerstein-Wiener系统的递归辨识算法[J].自动化学报,2014,40(2)：327-335.
[13]朱豫才.著.张湘平,虞水俊,孙志强,胡德文.译.过程控制的多变量系统辨识[M].长沙：国防科技大学出版社,2004.
[14]吴广玉.系统辨识与自适应[M].哈尔滨：哈尔滨工业大学出版社,1987.
[15]刘福才.非线性系统的模糊模型辨识及其应用[M].北京：国防工业出版社,2006.
[16]Zadeh L. A. From circuit theory to system theory [J]. Proc. IRE,1962,50(5): 856-865.
[17]Eykhoff P. System identification-parameter and state estimation[M]. John Wiley & Sons., INC,1974.
[18]Ljung L. Convergence analysis of parametric identification methods[J]. IEEE Transactions on Automatic Control,1978, AC-23:770-783.
[19]Ljung L. System identifieation:theory for the user [M].2nded. Englewood Cliffs, NJ, Prentice Hall,1999.
[20]侯媛彬,汪梅,王立琦.系统辨识及其MATLAB仿真[M].北京：科学出版社,2004.
[21]朱全民.非线性系统辨识[J].控制理论与应用,1994,11(6)：641-652.
[22]Giannakis G. B., Serpedin E. A bibliography on nonlinear system identification[J]. Signal Processing,2001,81(3):533-580.
[23]Billings S. A. Identification of nonlinear systems-a survey[J]. IEE Proceedings, 1980,6:272-285.
[24]Sjoberg J., Zhang Q. H., Ljung L., et al. Nonlinear black-box modeling in system identification:a unified overview[J]. Automatica,1995,31(12):1691-1724.
[25]Juditsky A., Hjalmarsson H., Benveniste A., et al. Nonlinear Black-box models in system identification:mathematical foundations [J]. Automatica,1995, 31(12):1725-1750.
[26]薛亚丽.热力过程多变量系统的优化设计[D].清华大学博士学位论文,2005.
[27]刘长良,于希宁,姚万业,等.基于遗传算法的火电厂热工过程模型辨识[J].中国电机工程学报,2003,23(3)：170-174.
[28]张小桃,倪维斗,李政,等.基于现场数据与神经网络的热工对象动态建模[J].热能动力工程,2005,20(1)：34-37.
[29]任青.智能优化理论及其在热工系统中的应用[D].华北电力大学硕士学位论文,2003.
[30]赵亮,雎刚.基于遗传算法的热工过程辨识[J].江苏电机工程,2006,25(3)：76-78.
[31]焦嵩鸣,韩璞,黄宇,等.模糊量子遗传算法及其在热工过程模型辨识中的应用[J].中国电机工程学报,2007,27(5)：87-92.
[32]刘志远.采用径向基函数神经网络的热工过程在线辨识方法[J].动力工程,2005,25(6)：844-848.
[33]张颖,冯纯伯.实现闭环系统参数一致估计的直接辨识方法[J].控制与决策,1996,11：628-632.
[34]张颖,冯纯伯.应用最小二乘法辨识闭环系统[J].自动化学报,1996,13：376-380.
[35]Gamier H., Gilson M., Zheng W. X. A bias-eliminated least-squares method for continuous-time model identification of closed-loop systems[J]. International Journal of Control,2000,73(1):38-48.
[36]Hwang S. H., Lai S. T. Use of two-stage least-squares algorithms for identification of continuous systems with time delay based on pulse response[J]. Automatica, 2004,40:1561-1568.
[37]Wang L., Leblanc A. Second-order nonlinear least squares estimation[J]. Annals of the Institute of Statistical Mathematics,2008,60:883-900.
[38]冯培梯.系统辨识[M].杭州：浙江大学出版社,1999.
[39]胡德文.非线性与多变量系统相关辨识[M].长沙：国防科技大学出版社,2001.
[40]康雷.人工神经网络在辨识与控制中的应用研究[D].东南大学博士学位论文,1999.
[41]李丽荣,沈春林,韩璞.基于BP网络的热工过程模型辨识方法[J].南京航空航天大学学报,2001,33(5)：499-502.
[42]汪镭,吴启迪.蚁群算法在系统辨识中的应用[J].自动化学报,2003,29(1)：102-109.
[43]杨维,李岐强.粒子群优化算法综述[J].中国工程科学,2004,6(5)：87-94.
[44]McCulloch W. S., Pitts W. A logic calculus of the ideas imminent in neurons activ-ity[J]. Bulletin of Math. Bio.,1943,5:115-133.
[45]Hebb. The Organization of Behavior[M]. Wiley, New York,1949.
[46]Rosenblatt F. Principles of Neurodynamics[M]. Spartan Book, New York,1962.
[47]Minsky M., Papert S. Perception[M]. The MIT Press, Cambridge, MA,1969.
[48]Hopfield J. J. Neural networks and physical system with emergent collective computational abilities[J]. Proc. Acad. Sci. USA.,1982,79:2554-2558.
[49]Hinton G. E., Sejnowski T. J., Ackley D. H. Boltzmann machine:constraint satis-faction networks and learn[M]. CMV-CS-84-119, Carneie-Mellon Uni.,1984.
[50]Ackley D. H., Hinton G E., Sejnowski T. J. Learning algorithm for Boltzmann machines[J]. Cognitive Science,1988,9:147-169.
[51]Rumelhart D. E., McClelland J. L. Parallel Distributed Processing[M]. MIT Press, 1986.
[52]王学武,谭得健.神经网络的应用与发展趋势[J].计算机工程与应用,2003,39(3)：98-113.
[53]李秀英,韩志刚.非线性系统辨识方法的新进展[J].自动化技术与应用,2004,23(10)：5-7.
[54]Prakriya M., Hatzinakos D. Blind identification of linear subsystems of LTI-ZMNL-LTI models with cyclostationary inputs[J]. IEEE Transactions on Signal Processing,1997,45(8):2023-2036.
[55]Greblicki W. Nonlinearity estimation in Hammerstein systems based on ordered observations[J]. IEEE Transactions on Signal Proeessing,1996,44(5):1224-1233.
[56]Narendra K. S., Gallman P. G. An iterative method for the identification of nonli-near systems using a Hammerstein model [J]. IEEE Transactions on Automatic Control,1966,11(3):546-550.
[57]Voros J. Parameter identification of discontinuous Hammerstein systems [J]. Au-tomatica,1997,33(6):1141-1146.
[58]Chang F., Luus R. A noniterative method for identification using Hammerstein model[J]. IEEE Transactions on Automatic Control,1971, AC-16:464-468.
[59]Boutayeb M., Rafaralahy H., Darouach M. A robust and recursive identification method for Hammerstein model [C]. Proceedings of IFAC World Congress, San Francisco,1996:447-452.
[60]Pawlak M. On the series expansion approach to the identification of Hammerstein systems[J]. IEEE Transactions on Automatic Control,1991,36(6):763-767.
[61]Bai E W., Fu M Y. A blind approach to Hammerstein model identification[J]. IEEE Transactions on Signal Processing,2002,50(7):1610-1619.
[62]Goethals I., Pelckmans K., Suykens J. A. K., et al. Subspace identification of Hammerstein systems using least squares support vector machines [J]. IEEE Transactions on Automatic Control,2005,50(10):1509-1519.
[63]Bai E. W. Identification of systems with hard input nonlinearities[C]. In Perspec-tives in Control, Moheimani R, Ed. New York:Springer Verlag,2001.
[64]Bai E. W. Frequency domain identification of Hammerstein models[J]. IEEE Transactions on Automatic Control,2003,48(4):530-542.
[65]Bai E. W., Li D. Convergence of the iterative Hammerstein system identification algorithm[J]. IEEE Transactions on Automatic Control,2004,49(11):1929-1940.
[66]Greblicki W. Continuous-time Hammerstein system identification from sampled data[J]. IEEE Transactions on Automatic Control,2006,51(7):1195-1200.
[67]黄正良,万百五,韩崇昭.辨识Hammerstein模型的两步法[J].控制理论与应用,1995,22(1)：34-39.
[68]徐桥南,袁振东.Hammerstein系统参数的集员辨识[J].控制理论与应用,1994,11(2)：221-215.
[69]孔金生,万百五.一种多输入单输出Hammerstein系统的集成辨识方法[J].控制理论与应用,2005,22(4)：517-519.
[70]张广莹,邓正隆.基于Adaline的双线性Hammerstein模型在线参数辨识[J].电机与控制学报,2003,7(3)：222-225.
[71]Sznaier M. Computational complexity analysis of set membership identification of Hammerstein and Wiener systems [J]. Automatic,2009,45(3):701-705.
[72]Sznaier M., Ma W. J., Camps O. I., et al. Risk adjusted set membership identifica-tion of Wiener systems[J]. IEEE Transactions on Automatic Control,2009, 54(5):1147-1152.
[73]徐小平,钱富才,王峰,等.基于改进粒子群算法的Hammerstein模型辨识[J].计算机工程,2008,34(14)：200-202.
[74]徐小平,钱富才,王峰.基于混合粒子群优化算法辨识Hammerstein模型[J].工程数学学报,2010,27(1)：47-52.
[75]Greblicki W. Nonparametric identification of Wiener systems[J]. IEEE Transac-tions on Information Theory,1992,38(10):1487-1493.
[76]Bai E. W., Reyland Jr. J. Towards identification of Wiener the least amount of a priori information on the nonlinearity[J]. Automatica,2008,44(4):910-919.
[77]Westwick D., Verhaegen M. Identifying MIMO Wiener systems using subspace model identification methods[J]. Signal Processing,1996,52(2):235-258.
[78]Billings S. A., Fakhouri S. Y. Identification of a class of nonlinear systems using correlation analysis[J]. Proceedings of IEE,1978,125(7):691-697.
[79]Crama P., Schoukens J. Initial estimates of Wiener and Hammerstein systems us-ing multisine excitation[J]. IEEE Transactions on Instrumentation and Measure-ment,2001,50:1791-1795.
[80]Hu X., Chen H. F. Strong consistence of recursive identification for Wiener sys-tems[J]. Automatica,2005,41(6):1905-1916.
[81]Cerone V., Regruto D. Parameter bounds evaluation of Wiener models with non-invertible polynomial nonlinearities[J]. Automatica,2006,42(10):1775-1781.
[82]Giri F., Rochdi Y., Chaoui F. Z. An analytic geometry approach to Wiener system frequency identification[J]. IEEE Transactions on Automatic Control,2009, 54(4):683-696.
[83]黄正良,吴坚,万百五.辨识Wiener模型的一种新方法[J].控制理论与应用,1996,13(3)：326-332.
[84]李世华,吴福保,李奇.一种基于动态人工神经网络的Wiener模型辨识[J].控制理论与应用,2000,17(1)：92-95.
[85]张广莹,邓正隆.基于小波分析的非线性系统Wiener模型辨识[J].电机与控制学报,2001,5(2)：95-97,128.
[86]许自豪,李嗣福,陈宗海.基于Lag-SBP的Wiener型非线性系统的辨识方法[J].系统仿真学报,2002,14(8)：1053-1055.
[87]黄毅卿,陈翰馥.子系统为ARMA和分段线性函数的Wiener系统的参数辨识[J].应用数学学报,2008,31(6)：961-980.
[88]Bai E. W. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems[J]. Automatica,1998,34(3):333-338.
[89]Bai E. W. A blind approach to the Hammerstein-Wiener model identification[J]. Automatica,2002,38(6):967-979.
[90]Zhu Y. C. Estimation of an N-L-N Hammerstein-Wiener model [J]. Automatica, 2002,38(9):1607-1614.
[91]Crama P., Schoukens J. Hammerstein-Wiener system estimator initialization[J]. Automatica,2004,40(9):1543-1550.
[92]Park H. C., Sung S. W., Lee J. Modeling of Hammerstein-Wiener Processes with special input test signals[J]. Industrial&Engineering Chemistry Research, 2006,45(3):1029-1038.
[93]桂卫华,宋海英,阳春华.Hammerstein-Wiener模型最小二乘向量机辨识及其应用[J].控制理论与应用,2008,25(3)：393-397.
[94]朱燕飞,谭洪舟,章云.一类非线性系统盲辨识算法及其仿真研究[J].系统仿真学报,2008,20(4)：3782-3784.
[95]Boutayeb M., Darouach M. Recursive identification method for MISO Wiener-Hammerstein model[J]. IEEE Transactions on Automatic Control,1995, 40(2):287-291.
[96]Bershad N. J., Bouchired S., Castanie F. Stochastic analysis of adaptive gradient identification of Wiener-Hammerstein systems for Gaussian inputs [J]. IEEE Transactions on Signal Processing,2000,48(2):557-560.
[97]柯晶,姜静,乔谊正.应用混合进化策略辨识Wiener-Hammerstein模型[J].系统工程与电子技术,2006,28(7)：1055-1063.
[98]Gong M., Jiao L., Zhang X. A population-based artificial immune system for numerical optimization [J]. Neurocomputing,2008,72(1-3):149-161.
[99]吴启迪,汪镭.群体智能(计算模式及应用)[M].南京：江苏教育出版社,2006.
[100]Bonabeau E., Dorigo M., Theraulaz G. Swarm Intelligence:From Natural to Artificial Systems [M]. Oxford University Press,1999.
[101]Bonabeau E., Dorigo M., Theraulaz G Inspiration for optimization from social insect behavior [J]. Nature,2000,406(6):39-42.
[102]Marinakis Y., Marinaki M. A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem [J]. Computers and Operations Research,2010,37(3):432-442.
[103]Altinoz O. T., Yilmaz A. E., Weber G W. Application of Chaos Embedded PSO for PID Parameter Tuning [J]. International Journal of Computers Communications & Control,2012,7(2):204-217.
[104]Sun J., Wu X., Palade V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization [J]. Information Sciences,2012, 193:81-103.
[105]Kuo R. J., Syu Y. J., Chen Z. Y., Tien F. C. Integration of particle swarm optimization and genetic algorithm for dynamic clustering [J]. Information Sciences,2012,195:124-140.
[106]Li X., Yao X. Cooperatively Coevolving Particle Swarms for Large Scale Optimization [J]. IEEE Transactions on Evolutionary Computation,2012,16(2): 210-224.
[107]Daneshyari M., Yen G G Constrained Multiple-Swarm Particle Swarm Optimization Within a Cultural Framework [J]. IEEE Transactions on systems Man and Cybernetics Part A-Systems and Humans,2012,42(2):475-490.
[108]Ghosh S., Das S., Kundu D., et al. An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization [J]. Neural Computation & Applications,2012,21(2):237-250.
[109]Alfi A. Particle Swarm Optimization Algorithm with Dynamic Intertia Weight for Online Parameter Identification Applied to Lorenz Chaotic System [J]. International Journal of Innovative Computing Information and Control,2012, 8(2):1191-1203.
[110]Deng W., Chen R., Gao J., et al. A novel parallel hybrid intelligence optimization algorithm for a function approximation problem [J]. Compuers & Mathematics with Applications 2012,63(1):325-336.
[111]Nafar M., Gharehpetian G B., Niknam T. Using Modified Fuzzy Particle Swarm Optimization Algorithm for Parameter Estimation of Surge Arresters Models [J]. International Journal of Innovative Computing Information and Control,2012, 8(1B):567-581.
[112]Zahara Er., Hu C. H. Solving constrained optimization problems with hybrid particle swarm optimization [J]. Engineering Optimization,2008,40(11): 1031-1049.
[113]Shi Y. H., Eberhart R. C. A modified particle swarm optimizer[C]. Proceedings of IEEE World Congress on Computational Intelligence, Anchorage,1998.
[114]Lovbjerg M., Rasmussen T. K., Krink T. Hybrid particle swarm optimizer with breeding and subpopulations[C]. Proceedings of the 3rd Genetic and Evolutionary Computation Conference,2001:469-476.
[115]Eberhart R. C., Shi Y. H. Comparing inertia weights and constriction factors in particle swarm optimization[C]. Proceedings of the International Congress on Evolutionary Computation, Piscataway:IEEE Press,2000:84-88.
[116]Moore P., Venayagamoorthy G. K. Evolving combinational logic circuits using a hybrid quantum evolution and particle swarm inspired algorithm[J]. Proceeding of the NASA/DoD Conference on Evolvable Hardware,2005:97-102.
[117]Mikki S. M., Kishk A. A.. Quantum particle swarm optimization for electromagnetics[J]. IEEE Transactions on Antennas and Propagation,2006, 54(10):2764-2775.
[118]于希宁,程锋章,朱丽玲,等.基于T-S模型的自适应神经模糊推理系统及其在热工过程建模中的应用[J].中国电机工程学报,2006,26(15)：78-82.
[119]张小桃,倪维斗,李政,郑松.基于现场数据热工对象建模的可辨识性[J].清华大学学报(自然科学版),2004,44(11)：1544-1547.
[120]张世华,雎刚.一种实数编码的自适应遗传算法及其在热工过程辨识中的应用研究[J].中国电机工程学报,2004,24(2)：210-214.
[121]刘红波,李少远,柴天佑.一种基于工况分解的热工过程非线性控制模型建立方法及应用[J].控制理论及应用,2004,21(5)：785-790.
[122]翟永杰,王学厚,张悦,等.火电厂分散控制系统原理及应用[M].北京：中国电力出版社,2010.
[123]王秀峰,劳育红.非线性动态系统模型结构确定和参数估计新算法[J].自动化学报,1992,18(4)：385-392.
[124]刘丽.基于T-S模型的热工系统模糊建模与控制[D].华北电力大学硕士学位论文,2005.
[125]王智琴.复杂热工系统的T-S模糊模型辨识研究[D].北京交通大学硕士学位论文,2009.
[126]周东华.控制系统的故障检测与诊断技术[M].北京：清华大学出版社,1994.
[127]疏松桂.控制系统可靠性分析与综合[M].北京：科学出版社,1992.
[128]Walseth J. A., Foss B. A., Lind M., et al. Models for diagnosis-application to a fertilizer plant[C]. Proceedings of the IFAC Symposium on on-line fault detection and supervision in the chemical process industries,1992,197-202.
[129]彭道刚.基于神经网络的电动执行器状态诊断[D].华北电力大学硕士学位论文,2003.
[130]孙远志.控制系统执行器故障诊断的研究及其应用[D].华北电力大学硕士学位论文,2004.
[131]张靖,刘少强.检测技术与系统设计[M].北京：中国电力出版社,2002.
[132]强锡富.传感器[M].北京：机械工业出版社,2001.
[133]赵祥生.热力过程自动化[M].北京：中国电力出版社,1996.
[134]袁去惑,孙吉星.热工测量及仪表[M].北京：水利电力出版社,1988.
[135]李阳春.自动控制理论在火电厂热工自动化中的应用[D].浙江大学博士学位论文,2001.
[136]蔡煜东,姚林声.传感器非线性校正的人工神经网络方法[J].仪器仪表学报,1994,15(3)：299-302.
[137]方一鸣,焦晓红,王洪瑞.一种单片机实现的热电偶测温的通用查表法[J].电气自动化,1998,5：37-39.
[138]王俊杰,张伟.最小二乘法在铂热电阻测温中的应用[J].仪表技术与传感器,1999,5：35-36.
[139]张洪涛,胡红丽,徐欣航,等.基于粒子群算法的火电厂热工过程模型辨识[J].热力发电,2010,39(5)：59-61.
[140]Michelle W. Complex-the science born at the edge of order and chaos[M]. Beijing: Joint Publishing Co.,1997.
[141]刘波.粒子群优化算法及其工程应用[M].北京：电子工业出版社,2010.
[142]江涛.Hammerstein模型辨识算法的研究[D].西安理工大学硕士学位论文,2010.
[143]寿纪麟.数学建模--方法与范例[M].西安：西安交通大学出版社,2008.
[144]李柏年,吴礼斌.MATLAB数据分析方法[M].北京：机械工业出版社,2012.
[145]Kennedy J., Eberhart R. Particle swarm optimization[A]. Proceedings of ICNN'95-International Conference on Neural Networks [C], New York, NY, USA, IEEE, 1995 (4):1942-1948.
[146]迟玉红,孙富春,王维军,等.基于空间缩放和吸引子的粒子群优化算法[J].计算机学报,2011,34(1)：115-129.
[147]Srinivasan A., Lakshmi P. Identification and control of Wiener type process applied to real-time heat exchanger [J]. Asia-Pacific Journal of Chemical Engineering, 2008,3(6):622-629.
[148]Srinivasan A., Lakshmi P. Wiener model based controller design for a physical heat exchanger [J]. Modeling, Measurement and Control B,2008,77(5-6):74-88.
[149]Hunt I. W., Korenberg M. J. The identification of nonlinear biological systems: Wiener and Hammerstein cascade models[J]. Biological Cybernetics,1986, 55:135-155.
[150]张艳,李少远,王笑波,等.基于粒子群优化的Wiener模型辨识与实例研究[J].控制理论与应用,2006,23(6)：991-995.
[151]Cybenko G. Approximation by superposition of a sigmoidal function[J]. Mathe-matics of Control, Signals, Systems,1989,2(4):303-314.
[152]祁永福.含分布式电源的配电网双层优化规划研究[D].华北电力大学硕士学位论文,2011.
[153]刘光磊,刘则良.弧齿锥齿轮传动误差曲线的双层优化法[J].农业机械学报,2012,7：223-227.
[154]姚伟锋,赵俊华,文福拴,等.基于双层优化的电动汽车充放电调度策略[J].电力系统自动化,2012,36(11)：30-36.
[155]张大波,李文沅,熊小伏.基于状态监测的多目标双层优化待修架空线选择模型[J].电力系统自动化,2013,37(2)：23-27.
[156]何瑞春,李引珍.时间依赖网络路径模型及双层优化智能算法研究[J].铁道学报,2008,30(1)：32-37.
[157]彭华,陈长,孙立军.网级路面管理系统中项目优化模型的双层优化[J].同济大学学报(自然科学版),2010,38(3)：380-385.
[158]沈佳宁,孙俊,须文波.运用QPSO算法进行系统辨识的研究[J].计算机工程与应用,2009,45(9)：67-70.
[159]Zhang Z. S. Quantum-behaved particle swarm optimization algorithm for econom-ic load dispatch of power system[J]. Expert Systems with Applications, 2010:1800-1803.
[160]Han K. H., Kim J. H. Genetic quantum algorithm and its application to combina-tional optimization problem[C]. Proceedings of the International Congress on Evolutionary Computation, IEEE Press,2000,1354-1360.
[161]李士勇,李盼池.求解连续空间优化问题的量子粒子群算法[J].量子电子学报,2007,24(5)：569-574.
[162]李士勇,李盼池.量子计算与量子优化算法[M].哈尔滨：哈尔滨工业大学出版社,2008.
[163]李盼池.量子计算及其在智能优化与控制中的应用[D].哈尔滨工业大学博士学位论文,2009.
[164]郝勇生,沈炯,侯子良,等.300MW循环流化床锅炉负荷、床温和床亚的动态特性分析[J].动力工程学报,2010,30(3)：175-179.
[165]何丽娜.基于现场数据和神经网络的主汽温系统建模方法研究[D].华北电力大学硕士学位论文,2009.
[166]张小桃,倪维斗,李政,等.基于现场数据的汽包压力动态建模研究与仿真[J].动力工程,2004,24(3)：370-374.
[167]陶哲,韩璞,刘丽.基于T-S模型的内模控制算法及其应用[J].计算机仿真,2006,23(12)：205-208.
[168]韩璞,林碧华,王东风,等.基于神经模糊系统的热工过程建模及预测[J].计算机仿真,2005,22(6)：139-144.
[169]Masry E., Cambanis S. Delta Modulation of the Wiener process[J]. IEEE Transac-tions on Communications,1975,11:1297-1300.
[2]方崇智,萧德云.过程辨识[M].北京：清华大学出版社,1988.
[3]冯恩民,修志龙.非线性发酵动力系统：辨识、控制与并行优化[M].北京：科学出版社,2012.
[4]吴立成,杨国胜,郐新凯,等.柔性臂机器人：建模、分析与控制[M].北京：高等教育出版社,2012.
[5]韩建达,何玉庆,赵新刚.移动机器人系统——建模、估计与控制[M].北京：科学出版社,2011.
[6]李洪心.可计算的—般均衡模型——建模与仿真[M].北京：机械工业出版社,2008.
[7]庞中红,崔红.系统辨识与自适应控制MATLAB仿真[M].北京：北京航空航天大学出版社,2013.
[8]刘金琨,沈晓蓉,赵龙.系统辨识理论及MATLAB仿真[M].北京：电子工业出版社,2013.
[9]曾凯文,文劲宇,程时杰,等.复杂电网连锁故障下的关键线路辨识[J].中国电机工程学报.2014,34(7)：1103-1112.
[10]贾杰,陈晨,曹姣,等.广义输出误差模型的两阶段最小二乘递推辨识[J].控制理论与应用,2014,31(2)：195-200.
[11]王建宏.丢失数据下的条件极大似然辨识[J].控制与决策,2014,29(2)：358-362.
[12]于丰,毛志忠,贾明兴,等.一种Hammerstein-Wiener系统的递归辨识算法[J].自动化学报,2014,40(2)：327-335.
[13]朱豫才.著.张湘平,虞水俊,孙志强,胡德文.译.过程控制的多变量系统辨识[M].长沙：国防科技大学出版社,2004.
[14]吴广玉.系统辨识与自适应[M].哈尔滨：哈尔滨工业大学出版社,1987.
[15]刘福才.非线性系统的模糊模型辨识及其应用[M].北京：国防工业出版社,2006.
[16]Zadeh L. A. From circuit theory to system theory [J]. Proc. IRE,1962,50(5): 856-865.
[17]Eykhoff P. System identification-parameter and state estimation[M]. John Wiley & Sons., INC,1974.
[18]Ljung L. Convergence analysis of parametric identification methods[J]. IEEE Transactions on Automatic Control,1978, AC-23:770-783.
[19]Ljung L. System identifieation:theory for the user [M].2nded. Englewood Cliffs, NJ, Prentice Hall,1999.
[20]侯媛彬,汪梅,王立琦.系统辨识及其MATLAB仿真[M].北京：科学出版社,2004.
[21]朱全民.非线性系统辨识[J].控制理论与应用,1994,11(6)：641-652.
[22]Giannakis G. B., Serpedin E. A bibliography on nonlinear system identification[J]. Signal Processing,2001,81(3):533-580.
[23]Billings S. A. Identification of nonlinear systems-a survey[J]. IEE Proceedings, 1980,6:272-285.
[24]Sjoberg J., Zhang Q. H., Ljung L., et al. Nonlinear black-box modeling in system identification:a unified overview[J]. Automatica,1995,31(12):1691-1724.
[25]Juditsky A., Hjalmarsson H., Benveniste A., et al. Nonlinear Black-box models in system identification:mathematical foundations [J]. Automatica,1995, 31(12):1725-1750.
[26]薛亚丽.热力过程多变量系统的优化设计[D].清华大学博士学位论文,2005.
[27]刘长良,于希宁,姚万业,等.基于遗传算法的火电厂热工过程模型辨识[J].中国电机工程学报,2003,23(3)：170-174.
[28]张小桃,倪维斗,李政,等.基于现场数据与神经网络的热工对象动态建模[J].热能动力工程,2005,20(1)：34-37.
[29]任青.智能优化理论及其在热工系统中的应用[D].华北电力大学硕士学位论文,2003.
[30]赵亮,雎刚.基于遗传算法的热工过程辨识[J].江苏电机工程,2006,25(3)：76-78.
[31]焦嵩鸣,韩璞,黄宇,等.模糊量子遗传算法及其在热工过程模型辨识中的应用[J].中国电机工程学报,2007,27(5)：87-92.
[32]刘志远.采用径向基函数神经网络的热工过程在线辨识方法[J].动力工程,2005,25(6)：844-848.
[33]张颖,冯纯伯.实现闭环系统参数一致估计的直接辨识方法[J].控制与决策,1996,11：628-632.
[34]张颖,冯纯伯.应用最小二乘法辨识闭环系统[J].自动化学报,1996,13：376-380.
[35]Gamier H., Gilson M., Zheng W. X. A bias-eliminated least-squares method for continuous-time model identification of closed-loop systems[J]. International Journal of Control,2000,73(1):38-48.
[36]Hwang S. H., Lai S. T. Use of two-stage least-squares algorithms for identification of continuous systems with time delay based on pulse response[J]. Automatica, 2004,40:1561-1568.
[37]Wang L., Leblanc A. Second-order nonlinear least squares estimation[J]. Annals of the Institute of Statistical Mathematics,2008,60:883-900.
[38]冯培梯.系统辨识[M].杭州：浙江大学出版社,1999.
[39]胡德文.非线性与多变量系统相关辨识[M].长沙：国防科技大学出版社,2001.
[40]康雷.人工神经网络在辨识与控制中的应用研究[D].东南大学博士学位论文,1999.
[41]李丽荣,沈春林,韩璞.基于BP网络的热工过程模型辨识方法[J].南京航空航天大学学报,2001,33(5)：499-502.
[42]汪镭,吴启迪.蚁群算法在系统辨识中的应用[J].自动化学报,2003,29(1)：102-109.
[43]杨维,李岐强.粒子群优化算法综述[J].中国工程科学,2004,6(5)：87-94.
[44]McCulloch W. S., Pitts W. A logic calculus of the ideas imminent in neurons activ-ity[J]. Bulletin of Math. Bio.,1943,5:115-133.
[45]Hebb. The Organization of Behavior[M]. Wiley, New York,1949.
[46]Rosenblatt F. Principles of Neurodynamics[M]. Spartan Book, New York,1962.
[47]Minsky M., Papert S. Perception[M]. The MIT Press, Cambridge, MA,1969.
[48]Hopfield J. J. Neural networks and physical system with emergent collective computational abilities[J]. Proc. Acad. Sci. USA.,1982,79:2554-2558.
[49]Hinton G. E., Sejnowski T. J., Ackley D. H. Boltzmann machine:constraint satis-faction networks and learn[M]. CMV-CS-84-119, Carneie-Mellon Uni.,1984.
[50]Ackley D. H., Hinton G E., Sejnowski T. J. Learning algorithm for Boltzmann machines[J]. Cognitive Science,1988,9:147-169.
[51]Rumelhart D. E., McClelland J. L. Parallel Distributed Processing[M]. MIT Press, 1986.
[52]王学武,谭得健.神经网络的应用与发展趋势[J].计算机工程与应用,2003,39(3)：98-113.
[53]李秀英,韩志刚.非线性系统辨识方法的新进展[J].自动化技术与应用,2004,23(10)：5-7.
[54]Prakriya M., Hatzinakos D. Blind identification of linear subsystems of LTI-ZMNL-LTI models with cyclostationary inputs[J]. IEEE Transactions on Signal Processing,1997,45(8):2023-2036.
[55]Greblicki W. Nonlinearity estimation in Hammerstein systems based on ordered observations[J]. IEEE Transactions on Signal Proeessing,1996,44(5):1224-1233.
[56]Narendra K. S., Gallman P. G. An iterative method for the identification of nonli-near systems using a Hammerstein model [J]. IEEE Transactions on Automatic Control,1966,11(3):546-550.
[57]Voros J. Parameter identification of discontinuous Hammerstein systems [J]. Au-tomatica,1997,33(6):1141-1146.
[58]Chang F., Luus R. A noniterative method for identification using Hammerstein model[J]. IEEE Transactions on Automatic Control,1971, AC-16:464-468.
[59]Boutayeb M., Rafaralahy H., Darouach M. A robust and recursive identification method for Hammerstein model [C]. Proceedings of IFAC World Congress, San Francisco,1996:447-452.
[60]Pawlak M. On the series expansion approach to the identification of Hammerstein systems[J]. IEEE Transactions on Automatic Control,1991,36(6):763-767.
[61]Bai E W., Fu M Y. A blind approach to Hammerstein model identification[J]. IEEE Transactions on Signal Processing,2002,50(7):1610-1619.
[62]Goethals I., Pelckmans K., Suykens J. A. K., et al. Subspace identification of Hammerstein systems using least squares support vector machines [J]. IEEE Transactions on Automatic Control,2005,50(10):1509-1519.
[63]Bai E. W. Identification of systems with hard input nonlinearities[C]. In Perspec-tives in Control, Moheimani R, Ed. New York:Springer Verlag,2001.
[64]Bai E. W. Frequency domain identification of Hammerstein models[J]. IEEE Transactions on Automatic Control,2003,48(4):530-542.
[65]Bai E. W., Li D. Convergence of the iterative Hammerstein system identification algorithm[J]. IEEE Transactions on Automatic Control,2004,49(11):1929-1940.
[66]Greblicki W. Continuous-time Hammerstein system identification from sampled data[J]. IEEE Transactions on Automatic Control,2006,51(7):1195-1200.
[67]黄正良,万百五,韩崇昭.辨识Hammerstein模型的两步法[J].控制理论与应用,1995,22(1)：34-39.
[68]徐桥南,袁振东.Hammerstein系统参数的集员辨识[J].控制理论与应用,1994,11(2)：221-215.
[69]孔金生,万百五.一种多输入单输出Hammerstein系统的集成辨识方法[J].控制理论与应用,2005,22(4)：517-519.
[70]张广莹,邓正隆.基于Adaline的双线性Hammerstein模型在线参数辨识[J].电机与控制学报,2003,7(3)：222-225.
[71]Sznaier M. Computational complexity analysis of set membership identification of Hammerstein and Wiener systems [J]. Automatic,2009,45(3):701-705.
[72]Sznaier M., Ma W. J., Camps O. I., et al. Risk adjusted set membership identifica-tion of Wiener systems[J]. IEEE Transactions on Automatic Control,2009, 54(5):1147-1152.
[73]徐小平,钱富才,王峰,等.基于改进粒子群算法的Hammerstein模型辨识[J].计算机工程,2008,34(14)：200-202.
[74]徐小平,钱富才,王峰.基于混合粒子群优化算法辨识Hammerstein模型[J].工程数学学报,2010,27(1)：47-52.
[75]Greblicki W. Nonparametric identification of Wiener systems[J]. IEEE Transac-tions on Information Theory,1992,38(10):1487-1493.
[76]Bai E. W., Reyland Jr. J. Towards identification of Wiener the least amount of a priori information on the nonlinearity[J]. Automatica,2008,44(4):910-919.
[77]Westwick D., Verhaegen M. Identifying MIMO Wiener systems using subspace model identification methods[J]. Signal Processing,1996,52(2):235-258.
[78]Billings S. A., Fakhouri S. Y. Identification of a class of nonlinear systems using correlation analysis[J]. Proceedings of IEE,1978,125(7):691-697.
[79]Crama P., Schoukens J. Initial estimates of Wiener and Hammerstein systems us-ing multisine excitation[J]. IEEE Transactions on Instrumentation and Measure-ment,2001,50:1791-1795.
[80]Hu X., Chen H. F. Strong consistence of recursive identification for Wiener sys-tems[J]. Automatica,2005,41(6):1905-1916.
[81]Cerone V., Regruto D. Parameter bounds evaluation of Wiener models with non-invertible polynomial nonlinearities[J]. Automatica,2006,42(10):1775-1781.
[82]Giri F., Rochdi Y., Chaoui F. Z. An analytic geometry approach to Wiener system frequency identification[J]. IEEE Transactions on Automatic Control,2009, 54(4):683-696.
[83]黄正良,吴坚,万百五.辨识Wiener模型的一种新方法[J].控制理论与应用,1996,13(3)：326-332.
[84]李世华,吴福保,李奇.一种基于动态人工神经网络的Wiener模型辨识[J].控制理论与应用,2000,17(1)：92-95.
[85]张广莹,邓正隆.基于小波分析的非线性系统Wiener模型辨识[J].电机与控制学报,2001,5(2)：95-97,128.
[86]许自豪,李嗣福,陈宗海.基于Lag-SBP的Wiener型非线性系统的辨识方法[J].系统仿真学报,2002,14(8)：1053-1055.
[87]黄毅卿,陈翰馥.子系统为ARMA和分段线性函数的Wiener系统的参数辨识[J].应用数学学报,2008,31(6)：961-980.
[88]Bai E. W. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems[J]. Automatica,1998,34(3):333-338.
[89]Bai E. W. A blind approach to the Hammerstein-Wiener model identification[J]. Automatica,2002,38(6):967-979.
[90]Zhu Y. C. Estimation of an N-L-N Hammerstein-Wiener model [J]. Automatica, 2002,38(9):1607-1614.
[91]Crama P., Schoukens J. Hammerstein-Wiener system estimator initialization[J]. Automatica,2004,40(9):1543-1550.
[92]Park H. C., Sung S. W., Lee J. Modeling of Hammerstein-Wiener Processes with special input test signals[J]. Industrial&Engineering Chemistry Research, 2006,45(3):1029-1038.
[93]桂卫华,宋海英,阳春华.Hammerstein-Wiener模型最小二乘向量机辨识及其应用[J].控制理论与应用,2008,25(3)：393-397.
[94]朱燕飞,谭洪舟,章云.一类非线性系统盲辨识算法及其仿真研究[J].系统仿真学报,2008,20(4)：3782-3784.
[95]Boutayeb M., Darouach M. Recursive identification method for MISO Wiener-Hammerstein model[J]. IEEE Transactions on Automatic Control,1995, 40(2):287-291.
[96]Bershad N. J., Bouchired S., Castanie F. Stochastic analysis of adaptive gradient identification of Wiener-Hammerstein systems for Gaussian inputs [J]. IEEE Transactions on Signal Processing,2000,48(2):557-560.
[97]柯晶,姜静,乔谊正.应用混合进化策略辨识Wiener-Hammerstein模型[J].系统工程与电子技术,2006,28(7)：1055-1063.
[98]Gong M., Jiao L., Zhang X. A population-based artificial immune system for numerical optimization [J]. Neurocomputing,2008,72(1-3):149-161.
[99]吴启迪,汪镭.群体智能(计算模式及应用)[M].南京：江苏教育出版社,2006.
[100]Bonabeau E., Dorigo M., Theraulaz G. Swarm Intelligence:From Natural to Artificial Systems [M]. Oxford University Press,1999.
[101]Bonabeau E., Dorigo M., Theraulaz G Inspiration for optimization from social insect behavior [J]. Nature,2000,406(6):39-42.
[102]Marinakis Y., Marinaki M. A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem [J]. Computers and Operations Research,2010,37(3):432-442.
[103]Altinoz O. T., Yilmaz A. E., Weber G W. Application of Chaos Embedded PSO for PID Parameter Tuning [J]. International Journal of Computers Communications & Control,2012,7(2):204-217.
[104]Sun J., Wu X., Palade V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization [J]. Information Sciences,2012, 193:81-103.
[105]Kuo R. J., Syu Y. J., Chen Z. Y., Tien F. C. Integration of particle swarm optimization and genetic algorithm for dynamic clustering [J]. Information Sciences,2012,195:124-140.
[106]Li X., Yao X. Cooperatively Coevolving Particle Swarms for Large Scale Optimization [J]. IEEE Transactions on Evolutionary Computation,2012,16(2): 210-224.
[107]Daneshyari M., Yen G G Constrained Multiple-Swarm Particle Swarm Optimization Within a Cultural Framework [J]. IEEE Transactions on systems Man and Cybernetics Part A-Systems and Humans,2012,42(2):475-490.
[108]Ghosh S., Das S., Kundu D., et al. An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization [J]. Neural Computation & Applications,2012,21(2):237-250.
[109]Alfi A. Particle Swarm Optimization Algorithm with Dynamic Intertia Weight for Online Parameter Identification Applied to Lorenz Chaotic System [J]. International Journal of Innovative Computing Information and Control,2012, 8(2):1191-1203.
[110]Deng W., Chen R., Gao J., et al. A novel parallel hybrid intelligence optimization algorithm for a function approximation problem [J]. Compuers & Mathematics with Applications 2012,63(1):325-336.
[111]Nafar M., Gharehpetian G B., Niknam T. Using Modified Fuzzy Particle Swarm Optimization Algorithm for Parameter Estimation of Surge Arresters Models [J]. International Journal of Innovative Computing Information and Control,2012, 8(1B):567-581.
[112]Zahara Er., Hu C. H. Solving constrained optimization problems with hybrid particle swarm optimization [J]. Engineering Optimization,2008,40(11): 1031-1049.
[113]Shi Y. H., Eberhart R. C. A modified particle swarm optimizer[C]. Proceedings of IEEE World Congress on Computational Intelligence, Anchorage,1998.
[114]Lovbjerg M., Rasmussen T. K., Krink T. Hybrid particle swarm optimizer with breeding and subpopulations[C]. Proceedings of the 3rd Genetic and Evolutionary Computation Conference,2001:469-476.
[115]Eberhart R. C., Shi Y. H. Comparing inertia weights and constriction factors in particle swarm optimization[C]. Proceedings of the International Congress on Evolutionary Computation, Piscataway:IEEE Press,2000:84-88.
[116]Moore P., Venayagamoorthy G. K. Evolving combinational logic circuits using a hybrid quantum evolution and particle swarm inspired algorithm[J]. Proceeding of the NASA/DoD Conference on Evolvable Hardware,2005:97-102.
[117]Mikki S. M., Kishk A. A.. Quantum particle swarm optimization for electromagnetics[J]. IEEE Transactions on Antennas and Propagation,2006, 54(10):2764-2775.
[118]于希宁,程锋章,朱丽玲,等.基于T-S模型的自适应神经模糊推理系统及其在热工过程建模中的应用[J].中国电机工程学报,2006,26(15)：78-82.
[119]张小桃,倪维斗,李政,郑松.基于现场数据热工对象建模的可辨识性[J].清华大学学报(自然科学版),2004,44(11)：1544-1547.
[120]张世华,雎刚.一种实数编码的自适应遗传算法及其在热工过程辨识中的应用研究[J].中国电机工程学报,2004,24(2)：210-214.
[121]刘红波,李少远,柴天佑.一种基于工况分解的热工过程非线性控制模型建立方法及应用[J].控制理论及应用,2004,21(5)：785-790.
[122]翟永杰,王学厚,张悦,等.火电厂分散控制系统原理及应用[M].北京：中国电力出版社,2010.
[123]王秀峰,劳育红.非线性动态系统模型结构确定和参数估计新算法[J].自动化学报,1992,18(4)：385-392.
[124]刘丽.基于T-S模型的热工系统模糊建模与控制[D].华北电力大学硕士学位论文,2005.
[125]王智琴.复杂热工系统的T-S模糊模型辨识研究[D].北京交通大学硕士学位论文,2009.
[126]周东华.控制系统的故障检测与诊断技术[M].北京：清华大学出版社,1994.
[127]疏松桂.控制系统可靠性分析与综合[M].北京：科学出版社,1992.
[128]Walseth J. A., Foss B. A., Lind M., et al. Models for diagnosis-application to a fertilizer plant[C]. Proceedings of the IFAC Symposium on on-line fault detection and supervision in the chemical process industries,1992,197-202.
[129]彭道刚.基于神经网络的电动执行器状态诊断[D].华北电力大学硕士学位论文,2003.
[130]孙远志.控制系统执行器故障诊断的研究及其应用[D].华北电力大学硕士学位论文,2004.
[131]张靖,刘少强.检测技术与系统设计[M].北京：中国电力出版社,2002.
[132]强锡富.传感器[M].北京：机械工业出版社,2001.
[133]赵祥生.热力过程自动化[M].北京：中国电力出版社,1996.
[134]袁去惑,孙吉星.热工测量及仪表[M].北京：水利电力出版社,1988.
[135]李阳春.自动控制理论在火电厂热工自动化中的应用[D].浙江大学博士学位论文,2001.
[136]蔡煜东,姚林声.传感器非线性校正的人工神经网络方法[J].仪器仪表学报,1994,15(3)：299-302.
[137]方一鸣,焦晓红,王洪瑞.一种单片机实现的热电偶测温的通用查表法[J].电气自动化,1998,5：37-39.
[138]王俊杰,张伟.最小二乘法在铂热电阻测温中的应用[J].仪表技术与传感器,1999,5：35-36.
[139]张洪涛,胡红丽,徐欣航,等.基于粒子群算法的火电厂热工过程模型辨识[J].热力发电,2010,39(5)：59-61.
[140]Michelle W. Complex-the science born at the edge of order and chaos[M]. Beijing: Joint Publishing Co.,1997.
[141]刘波.粒子群优化算法及其工程应用[M].北京：电子工业出版社,2010.
[142]江涛.Hammerstein模型辨识算法的研究[D].西安理工大学硕士学位论文,2010.
[143]寿纪麟.数学建模--方法与范例[M].西安：西安交通大学出版社,2008.
[144]李柏年,吴礼斌.MATLAB数据分析方法[M].北京：机械工业出版社,2012.
[145]Kennedy J., Eberhart R. Particle swarm optimization[A]. Proceedings of ICNN'95-International Conference on Neural Networks [C], New York, NY, USA, IEEE, 1995 (4):1942-1948.
[146]迟玉红,孙富春,王维军,等.基于空间缩放和吸引子的粒子群优化算法[J].计算机学报,2011,34(1)：115-129.
[147]Srinivasan A., Lakshmi P. Identification and control of Wiener type process applied to real-time heat exchanger [J]. Asia-Pacific Journal of Chemical Engineering, 2008,3(6):622-629.
[148]Srinivasan A., Lakshmi P. Wiener model based controller design for a physical heat exchanger [J]. Modeling, Measurement and Control B,2008,77(5-6):74-88.
[149]Hunt I. W., Korenberg M. J. The identification of nonlinear biological systems: Wiener and Hammerstein cascade models[J]. Biological Cybernetics,1986, 55:135-155.
[150]张艳,李少远,王笑波,等.基于粒子群优化的Wiener模型辨识与实例研究[J].控制理论与应用,2006,23(6)：991-995.
[151]Cybenko G. Approximation by superposition of a sigmoidal function[J]. Mathe-matics of Control, Signals, Systems,1989,2(4):303-314.
[152]祁永福.含分布式电源的配电网双层优化规划研究[D].华北电力大学硕士学位论文,2011.
[153]刘光磊,刘则良.弧齿锥齿轮传动误差曲线的双层优化法[J].农业机械学报,2012,7：223-227.
[154]姚伟锋,赵俊华,文福拴,等.基于双层优化的电动汽车充放电调度策略[J].电力系统自动化,2012,36(11)：30-36.
[155]张大波,李文沅,熊小伏.基于状态监测的多目标双层优化待修架空线选择模型[J].电力系统自动化,2013,37(2)：23-27.
[156]何瑞春,李引珍.时间依赖网络路径模型及双层优化智能算法研究[J].铁道学报,2008,30(1)：32-37.
[157]彭华,陈长,孙立军.网级路面管理系统中项目优化模型的双层优化[J].同济大学学报(自然科学版),2010,38(3)：380-385.
[158]沈佳宁,孙俊,须文波.运用QPSO算法进行系统辨识的研究[J].计算机工程与应用,2009,45(9)：67-70.
[159]Zhang Z. S. Quantum-behaved particle swarm optimization algorithm for econom-ic load dispatch of power system[J]. Expert Systems with Applications, 2010:1800-1803.
[160]Han K. H., Kim J. H. Genetic quantum algorithm and its application to combina-tional optimization problem[C]. Proceedings of the International Congress on Evolutionary Computation, IEEE Press,2000,1354-1360.
[161]李士勇,李盼池.求解连续空间优化问题的量子粒子群算法[J].量子电子学报,2007,24(5)：569-574.
[162]李士勇,李盼池.量子计算与量子优化算法[M].哈尔滨：哈尔滨工业大学出版社,2008.
[163]李盼池.量子计算及其在智能优化与控制中的应用[D].哈尔滨工业大学博士学位论文,2009.
[164]郝勇生,沈炯,侯子良,等.300MW循环流化床锅炉负荷、床温和床亚的动态特性分析[J].动力工程学报,2010,30(3)：175-179.
[165]何丽娜.基于现场数据和神经网络的主汽温系统建模方法研究[D].华北电力大学硕士学位论文,2009.
[166]张小桃,倪维斗,李政,等.基于现场数据的汽包压力动态建模研究与仿真[J].动力工程,2004,24(3)：370-374.
[167]陶哲,韩璞,刘丽.基于T-S模型的内模控制算法及其应用[J].计算机仿真,2006,23(12)：205-208.
[168]韩璞,林碧华,王东风,等.基于神经模糊系统的热工过程建模及预测[J].计算机仿真,2005,22(6)：139-144.
[169]Masry E., Cambanis S. Delta Modulation of the Wiener process[J]. IEEE Transac-tions on Communications,1975,11:1297-1300.