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

来源：

NEUROQUANTOLOGY | 2008年 / 6卷 / 04期

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

页码：387 / 396

页数：10

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