Study on simulation of RBF NN identification method based on adaptive structural optimization

被引：0

作者：

Xiao, Yun-shi ^{[1
]}

Ding, Hong-kai ^{[1
]}

Yue, Ji-guang ^{[1
]}

机构：

[1] Tongji Univ, Sch Elect & Informat Engn, Shanghai 200092, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

nonlinear system identification; matrix encoding; particle swarm optimization; Structure Risk Minimization; RBF NN;

D O I：

10.1109/WCICA.2008.4594207

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A novel nonlinear system identification method based on adaptive structural optimization of radial basis function neural network using particle swarm optimization algorithm is proposed in this paper. Using matrix encoding strategy, all parameters such as hidden layer nodes number, central position, directional width, weights of RBF NN are estimated dynamically in global. Under the framework of Structure Risk Minimization, the RBF NN model with excellent approximation ability can be dredged with prediction risk fitness. The simulation results show the effectiveness of this method.

引用

页码：8174 / 8178

页数：5

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