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
收藏
页码：476 / 481
页数：6
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