WIENER MODEL IDENTIFICATION BASED ON ADAPTIVE PARTICLE SWARM OPTIMIZATION

摘要：

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.

关键词： System identification nonlinear system adaptive particle swarm optimization Wiener model

作者: ZHI-XIANG HOU

作者单位: College of Automobile and Mechnical Engineering, Changsha University of Science and Technology, Changsha 410076, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1041-1045

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）