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

Author

Chun-tao, Man ; Kun, Wang ; Li-yong, Zhang

Author_Institution

Sch. of Autom., Harbin Univ. of Sci. & Technol., Harbin, China

Volume

4

fYear

2009

fDate

March 31 2009-April 2 2009

Firstpage

641

Lastpage

645

Abstract

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.

Keywords

learning (artificial intelligence); particle swarm optimisation; radial basis function networks; resource allocation; PSO; RAN; RBF neural network; central position selection; neural network training; particle swarm optimisation; radial basis function; resource allocation; simulation study; Computational modeling; Computer science; Computer simulation; Feedforward systems; Function approximation; Neural networks; Particle swarm optimization; Radial basis function networks; Radio access networks; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Engineering, 2009 WRI World Congress on

Conference_Location

Los Angeles, CA

Print_ISBN

978-0-7695-3507-4

Type

conf

DOI

10.1109/CSIE.2009.76

Filename

5171074