Wiener model identification based on adaptive particle swarm optimization

A novel approach for nonlinear system identification is proposed based on adaptive particle swarm optimization in this paper. Particle swarm optimization is demonstrated as efficient global search method for complex surfaces, and in order to quick the convergence speed, an adaptive particle swarm optimization strategy was introduced. The proposed method formulates the nonlinear system identification as an optimization problem in parameter space, and then adaptive particle swarm optimization are used in the optimization process to find the estimation values of the parameters respectively. Application to Wiener model, in which the nonlinear static subsystems and linear dynamic are separated in different order, is studied and compared with other methods and the simulation results show the identification by adaptive particle swarm optimization is very effective and superior accuracy.

[1]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[2]  Wenbo Xu,et al.  Nonlinear System Identification of Hammerstien and Wiener Model Using Swarm Intelligence , 2006, 2006 IEEE International Conference on Information Acquisition.

[3]  K. Uosaki,et al.  System parameter estimation by evolutionary strategy , 1996, Proceedings of the 35th SICE Annual Conference. International Session Papers.

[4]  L. Ljung,et al.  Maximum likelihood identification of Wiener models with a linear regression initialization , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[5]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.