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

Author

Ying, Liu ; Jie, Liu ; Bing, Yan ; Hongwei, Mao ; Hongxia, Pan ; Yan, Zhang

Author_Institution

Tianjin Key Lab. of High Speed Cutting & Precision Machining, Tianjin Univ. of Technol. & Educ., Tianjin, China

Volume

1

fYear

2009

fDate

19-21 May 2009

Firstpage

222

Lastpage

225

Abstract

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.

Keywords

image classification; learning (artificial intelligence); particle swarm optimisation; wavelet transforms; condition monitoring; fault diagnosis; iris classification experiment; modified particle swarm optimization compound model; pattern recognition; wavelet neural-network training method; Condition monitoring; Fault diagnosis; Intelligent networks; Intelligent systems; Laboratories; Neural networks; Particle swarm optimization; Pattern recognition; Stochastic processes; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2009. GCIS '09. WRI Global Congress on

Conference_Location

Xiamen

Print_ISBN

978-0-7695-3571-5

Type

conf

DOI

10.1109/GCIS.2009.419

Filename

5208984