RBF neural network based on particle swarm optimization

Authors:
Yuxiang Shao;Qing Chen;Hong Jiang
Affiliations:
School of Computer Sciences, China University of Geosciences, Wuhan, Hubei, China;Hubei Province Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, Hubei, China;Hubei Province Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, Hubei, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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Abstract

This paper develops a RBF neural network based on particle swarm optimization (PSO) algorithm It is composed of a RBF neural network, whose parameters including clustering centers, variances of Radial Basis Function and weights are optimized by PSO algorithm Therefore it has not only simplified the structure of RBF neural network, but also enhanced training speed and mapping accurate The performance and effectiveness of the proposed method are evaluated by using function simulation and compared with RBF neural network The result shows that the optimized RBF neural network has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results.