Wavelet Neural Network Based on Modified PSO and Its Application in Pattern Recognition

被引：3

作者：

Ying, Liu ^{[1
,2
]}

Jie, Liu ^{[3
]}

Bing, Yan ^{[1
]}

Mao Hongwei ^{[2
]}

Pan Hongxia ^{[2
]}

Yan, Zhang ^{[1
]}

机构：

[1] Tianjin Univ Technol & Educ, Tianjin Key Lab High Speed Cutting & Precis Machi, Tianjin, Peoples R China

[2] North Univ China, Coll Mech Engn & Automat, Taiyuan, Peoples R China

[3] Changan Univ, Sch Engn Machinery, Xian, Peoples R China

来源：

PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL I | 2009年

关键词：

D O I：

10.1109/GCIS.2009.419

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A modified particle swarm optimization compound model PSO with stochastic inertia weigh is put forward and used to optimize the parameters of wavelet neural network. The trained wavelet neural-network is applied to the Iris classification experiment The experimental result indicates that the wavelet neural-network training method based on the modified PSO is effective. This is an available approach to solve some problems, such as the pattern recognition, condition monitoring and fault diagnosis, etc

引用

页码：222 / +

页数：2

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