Training radial basis function networks with particle swarms

被引：0
|
作者：
Liu, Y [1 ]
Zheng, Q
Shi, ZW
Chen, JY
机构：
[1] Xi An Jiao Tong Univ, Dept Comp Sci, Xian 710049, Peoples R China
[2] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 | 2004年 / 3173卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, Particle Swarm Optimization (PSO) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, PSO achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.
引用
收藏
页码：317 / 322
页数：6
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