Hybrid Algorithm for Hammerstein System Identification Using Genetic Algorithm and Particle Swarm Optimization

This paper presents a method of model selection and identification of Hammerstein systems by hybridization of the genetic algorithm (GA) and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. On the other hand, the adjusting parameters of the ACF model structure are properly selected by the hybrid algorithm of the GA and PSO, where the Akaike information criterion is utilized as the evaluation value function. Simulation results are shown to demonstrate the effectiveness of the proposed hybrid algorithm. Keywords—Hammerstein system, identification, automatic choosing function model, genetic algorithm, particle swarm optimization.

[1]  F. Alonge,et al.  Nonlinear Modeling of DC/DC Converters Using the Hammerstein's Approach , 2007, IEEE Transactions on Power Electronics.

[2]  安田 恵一郎,et al.  適応型Particle Swarm Optimizationに関する基礎的検討 , 2004 .

[3]  Keiichiro Yasuda,et al.  A Basic Study of the Adaptive Particle Swarm Optimization , 2005 .

[4]  O. Nelles.  Nonlinear System Identification , 2001 .

[5]  Fan-Chu Kung,et al.  Analysis and identification of Hammerstein model non-linear delay systems using block-pulse function expansions , 1986 .

[6]  Stephen A. Billings,et al.  Identification of systems containing linear dynamic and static nonlinear elements , 1982, Autom..

[7]  Shuichi Adachi,et al.  Generalized Predictive Control System Design Based on Non-Linear Identification by Using Hammerstein Model , 1995 .

[8]  M. Nazmul Karim,et al.  A New Method for the Identification of Hammerstein Model , 1997, Autom..

[9]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[10]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  K. Uosaki,et al.  Evolutionary computation approach to Wiener model identification , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  F.M. Ghannouchi,et al.  Augmented hammerstein predistorter for linearization of broad-band wireless transmitters , 2006, IEEE Transactions on Microwave Theory and Techniques.

[13]  Visakan Kadirkamanathan,et al.  Stability analysis of the particle dynamics in particle swarm optimizer , 2006, IEEE Transactions on Evolutionary Computation.

[14]  H. Akaike.  A new look at the statistical model identification , 1974 .

[15]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[18]  Hitoshi Takata,et al.  Structure Selection and Identification of Hammerstein Type Nonlinear Systems Using Automatic Choosing Function Model and Genetic Algorithm , 2005, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[19]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[20]  H. Takata.  An automatic choosing control for nonlinear systems , 1996, Proceedings of 35th IEEE Conference on Decision and Control.