东华大学学报（英文版）2005，Vol.22Issue(1)：37-40,4.
A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy
A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy
GONG Xin-bao 1ZANG Xiao-gang 1ZHOU Xi-lang1
作者信息
- 1. Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200030
- 折叠
摘要
Abstract
A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances.关键词
radial basis function networks/immune algorithm/least square methodKey words
radial basis function networks/immune algorithm/least square method引用本文复制引用
GONG Xin-bao,ZANG Xiao-gang,ZHOU Xi-lang..A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy[J].东华大学学报（英文版）,2005,22(1):37-40,4.
