Evolving RBF neural networks for pattern classification

被引：0
|
作者：
Qin, Z [1 ]
Chen, JY
Liu, Y
Lu, J
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：
COMPUTATIONAL INTELLIGENCE AND SECURITY, PT 1, PROCEEDINGS | 2005年 / 3801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：
When a radial-basis function neural network (RBFNN) is used for pattern classification, the problem involves designing the topology of RBFNN and also its centers and widths. In this paper, we present a particle swarm optimization (PSO) learning algorithm to automate the design of RBF networks, to solve pattern classification problems. Simulation results for benchmark problems in the pattern classification area show that the PSO-RBF outperforms two other learning algorithms in terms of network size and generalization performance.
引用
收藏
页码：957 / 964
页数：8
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