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A New Training Algorithm for RBF Neural Network Based on PSO and Simulation Study

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3 Author(s)
Man Chun-tao ; Sch. of Autom., Harbin Univ. of Sci. & Technol., Harbin, China ; Wang Kun ; Zhang Li-yong

Being difficult to determine hidden unitspsilas number and unsuitable to select central position in radial basis function (RBF) layer, particle swarm optimization and resource allocation (RAN) were proposed for training RBF neural networks. First, determine unitspsilas number in RBF layer using RAN. Then, optimize RBF parameters such as central position, width and weights based on PSO. The simulation results show that the new method has better approximation ability, the shorter time and the higher precision.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:4 )

Date of Conference:

March 31 2009-April 2 2009