Study of Fault Classification Based on PSO Wavelet Neural Network-Reference-Cited by-同舟云学术

Study of Fault Classification Based on PSO Wavelet Neural Network

Published:2011-03-28 Issue: Volume:52-54 Page:476-481
ISSN:1662-7482
Container-title:Applied Mechanics and Materials
language:
Short-container-title:AMM

Author:

Pan Hong Xia¹,Huang Jin Ying¹

Affiliation:

1. North University of China

Abstract

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.

Publisher

Trans Tech Publications, Ltd.

Link

https://www.scientific.net/AMM.52-54.476.pdf

Reference186 articles.

1. Zhang Q, Benveniste A: Wavelet Network, Proc. of IEEE Trans. on Neural Network, Vol. 3 (6), pp.889-898, (1992).

2. Szu H, Telfer B, Kadambe S: Neural Network adaptive Wavelets for Signal representation and classification, Optical Engineering, Vol. 31 (9) 1907-1916. (1992).

3. Hu Kuangku, Li Shuyu, Su WanFang: The Wavelet Neural Network and Its Application, Chinese Stereoscope and image analysis, Vol. 6(4), pp.239-245, (2001).

4. Zhao Xuezhi, Zhou Chunhua etc.: The parameter initialization research of wavelet neural network, Journal of Southern China Theory& Skill University, Vol. 31(2), pp.77-84, (2003).

5. Yoshida H, Kawata K, etc.: A particle swarm optimization for reactive power and voltage control considering voltage security assessment. Transactions of the Institute of Electrical Engineers of Japan, Vol. 119 - B (12), pp.1462-1469, (1999).