|
|
|
| New Method for Identification of Hammerstein Model |
| LI Wen-jiang,LIN Si-jian,WANG Xuan |
| School of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China |
|
|
|
|
Abstract In order to improve the identification accuracy of nonlinear Hammerstein model, a new method of the hybrid optimization algorithm identifying the nonlinear model is put forward. The basic idea of the algorithm is to put the identification problem of parameters in the nonlinear system into a parameter space function optimization problem, and then using hybrid algorithm of the genetic algorithm and the improved particle swarm optimization algorithm to obtain the optimal solution of parameters problem. Finally, the simulation results show that the method for nonlinear identification has good effectiveness and robustness,get a good recognition effect, and is a feasible method to solve the problem of nonlinear recognition.
|
|
|
|
|
|
［1］Goodwin G C, Payne R L. Dynamic System Identificatio:Experiment Design and Data Analysis［M］. New York:Academic Press,1977.
［2］徐小平,王峰,胡钢.系统辨识研究的现状［J］.现代电子技术, 2007, 30(15):119-123.
［3］林卫星,等.应用粒子群优化算法辨识 Hammerstein 模型［Ｊ］.仪器仪表学报, 2006, 27(1) : 75-79.
［4］Sznaier M. Computational Complexity Analysis of Set Mem-bership Identification of Hammerstein and Wiener Systems［J］.Automatica, 2009, 45(3): 701-705.
［5］Chen H F. Strong Consistency of Recursive Identification for Hammerstein Systems with Discontinuous Piecewise-linear Memory-less Block［J］. IEEE Transactions on Automatic Control, 2005, 50(10): 1612-1617.
［6］Vanbeylen L, Pintelon R, Schoukens J. Blind Maximum LikelihoodIdentification of Hammerstein Systems［J］.Automatica, 2008, 44(12): 3139-3146.
［7］习毅,潘丰.基于 Hammerstein 模型的非线性自适应预测函数控制［Ｊ］.系统工程与电子技术, 2008, 30(11): 2237-2240.
［8］丁宝苍,李少远.具有约束的 Hammerstein 非线性控制系统的设计与分析［Ｊ］.控制与决策, 2003, 18(1): 24-28.
［9］陈思海.基于 Hammerstein 模型的非线性系统辨识方法研究［Ｊ］.化工自动化及仪表, 2011, 38( 1) : 37-39.
［10］孙俊,等.量子行为粒子群优化原理及其应用［M］. 北京: 清华大学出版社, 2011.
［11］Kennedy J, Eberhart R C. Particle Swarm Optimization［C］// IEEE. International Conference on Neural Networks. Perth, Australia, 1995,1942-1948.
［12］陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究［Ｊ］.西安交通大学学报, 2006,40(1): 53-56, 61.
［13］王树青,等. 一种基于奇异值分解技术的模型定阶方法［Ｊ］.振动与冲击, 2012,31(15):87-91.
［14］张泉灵,王树青.基于Hammerstein模型的非线性预测函数控制［Ｊ］.浙江大学学报(工学版), 2002,36(2):119-122. |
|
|
|
2015, Vol. 36 
