Home > Table of Contents


Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 137-140

Improved RBF Neural Network for Nonlinear Identification System

Jian Guo, Jing Gong, and Jinbang Xu

Full text: PDF


Standard particle swarm optimization (SPSO) algorithm was modified by escape strategy of the particle velocity, and an escape PSO (EPSO) was proposed to overcome the shortcomings of being trapped in local optima because of premature convergence. To enhance the performance of radial basis function (RBF) neural network, the EPSO is combined with RBF neural network to form a EPSON hybrid algorithm. Compared with the hybrid algorithm of BP neural network (PSOBP), the experiment results show that EPSON has less adjustable parameters, faster convergence speed and higher precision in the nondifferentiable function identification.

Index Terms

escape strategy, non-differentiable function identification, EPSO, radial basis function

Copyright @ 2009 ACADEMY PUBLISHER All rights reserved