Neural network training using stochastic PSO

Authors:
Xin Chen;Yangmin Li
Affiliations:
Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao S. A. R., P.R. China;Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao S. A. R., P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

Particle swarm optimization is widely applied for training neural network. Since in many applications the number of weights of NN is huge, when PSO algorithms are applied for NN training, the dimension of search space is so large that PSOs always converge prematurely. In this paper an improved stochastic PSO (SPSO) is presented, to which a random velocity is added to improve particles' exploration ability. Since SPSO explores much thoroughly to collect information of solution space, it is able to find the global best solution with high opportunity. Hence SPSO is suitable for optimization about high dimension problems, especially for NN training.