Pattern classification with a PSO optimization based elliptical basis function neural networks

被引：3
|
作者：
Du, Ji-Xiang [1 ,2 ]
Huang, De-Shuang [2 ]
Wang, Zeng-Fu [2 ]
机构：
[1] Univ Sci & Technol China, Dept Automat, Hefei, Peoples R China
[2] Huaqiao Univ, Dept Comp Sci & Technol, Quanzhou, Peoples R China
来源：
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年
基金：
中国博士后科学基金;
关键词：
D O I：
10.1109/CEC.2007.4424672
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, a novel model of elliptical basis function neural networks (EBFNN) based:on a hybrid optimization algorithm is proposed. Firstly a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer, of the EBFNN, i.e.. an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO). algorithm. And the shape parameters of kernel function for the hidden layer are also optimized by the PSO simultaneously. Finally, the hybrid learning algorithm (HLA) is further. applied to adjust the hidden centers and the shape parameters of kernel function for die hidden layer, The experimental results demonstrated the proposed hybrid, optimization algorithm for the EBFNN model is feasible and efficient. and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.
引用
收藏
页码：1654 / +
页数：3
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