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Study of Fault Classification Based on PSO Wavelet Neural Network

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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.

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Advances in Mechanical Engineering (ICME) View Preview

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Periodical:

Applied Mechanics and Materials (Volumes 52-54)

Pages:

476-481

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.52-54.476

Citation:

Cite this paper

Online since:

March 2011

Authors:

Hong Xia Pan, Jin Ying Huang

Keywords:

Gearbox Fault, Particle Swarm Optimization Algorithm (PSO), Pattern Classification, Wavelet Neural Network (WNN)

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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