Parameter Identification of Wiener Model with Discontinuous Nonlinearities Using Hybrid Simplex Search and Particle Swarm Optimization

被引:0
作者
Tang, Yinggan [1 ]
Qiao, Leijie [1 ]
Guan, Xinping [1 ]
机构
[1] Yanshan Univ, Key Lab Ind Comp Control Engn Hebei Prov, Qinhuangdao 06004, Hebei, Peoples R China
关键词
Wiener model; parameter identification; discontinuous nonlinearities; hybrid NM-PSO algorithm;
D O I
暂无
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
This paper deals with the parameter identification of Wiener model with discontinuous nonlinear. The parameter identification problem is converted to an optimal problem with a suitable objective function. A hybrid optimal method, which integrates the Nelder-Mead simplex search and particle swarm optimization (NM-PSO), is used to optimize the objective function. The hybrid optimal method NM-PSO can get obtain the global optimal solution with fast convergent rate. Two illustrative examples are included to demonstrate the effectiveness and feasible of the proposed identification method.
引用
收藏
页码:387 / 396
页数:10
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