Study of Fault Classification Based on PSO Wavelet Neural Network

被引：0

作者：

Pan Hongxia ^{[1
]}

Huang Jinying ^{[1
]}

机构：

[1] N Univ China, Sch Mech Engn & Automat, Taiyuan, Peoples R China

来源：

ADVANCES IN MECHANICAL ENGINEERING, PTS 1-3 | 2011年 / 52-54卷

关键词：

wavelet neural-network; pattern classification; particle swarm optimization (PSO); gearbox fault;

D O I：

10.4028/www.scientific.net/AMM.52-54.476

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In this paper, a novel studying-training algorithm of wavelet neural-network based on particle swarm optimization (PSO) was presented. Then it was compared with the traditional gradient descent algorithm by the fault classification experiment. The simulation result get a conclusion that the wavelet neural-network trained by PSO not only reduces the iterations, but also get the better convergence precision. It is further indicated that PSO algorithm is fit to train the wavelet neural-network and the optimized network has good signal classification ability.

引用

页码：476 / 481

页数：6

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