Nonlinear System Identification of Hammerstien and Wiener Model Using Swarm Intelligence

摘要：

In this paper a novel approach for nonlinear system identification is proposed using Particle Swarm Optimization (PSO) and Quantum-behaved Particle Swarm Optimization (QPSO). PSO and QPSO algorithm, the most successful and representative swarm intelligence optimization techniques,were demonstrated as an efficient global search method for complex surfaces. The proposed method formulates the nonlinear system identification as an optimization problem in parameter space, and then PSO and QPSO are used in the optimization process to find the optimal estimation of the system parameters respectively. Application to Hammerstein and Wiener nonlinear model, in which the nonlinear static subsystems and linear dynamic subsystems are separated in different order, is studied and the simulation results show that the identification by swarm intelligence is easy in computation and superior in accuracy.

作者: J.Liu Wenbo.Xu J.Sun

会议类型: 国际会议

会议名称: 2006 IEEE International Conference on Information Acquisition

会议地点: 山东威海

会议语种:英文

页码: 1219-1223

在线出版日期: 2006-08-20（万方平台首次上网日期，不代表论文的发表时间）