Training RBF neural network with hybrid particle swarm optimization

Authors:
Haichang Gao;Boqin Feng;Yun Hou;Li Zhu
Affiliations:
School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China;School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China;School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China;School of Software Engineering, Xi’an Jiaotong University, Xi’an, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

The particle swarm optimization (PSO) has been used to train neural networks. But the particles collapse so quickly that it exits a potentially dangerous stagnation characteristic, which would make it impossible to arrive at the global optimum. In this paper, a hybrid PSO with simulated annealing and Chaos search technique (HPSO) is adopted to solve this problem. The HPSO is proposed to train radial basis function (RBF) neural network. Benchmark function optimization and dataset classification problems (Iris, Glass, Wine and New-thyroid) experimental results demonstrate the effectiveness and efficiency of the proposed algorithm.