Radial basis function neural network based on ant colony optimization

被引：6
|
作者：
Man Chun-tao [1 ]
Li Xiao-xia [1 ]
Zhang Li-yong [2 ]
机构：
[1] Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
[2] Harbin Univ Sci & Technol, Sch Measurent Control Tech & Commun Engn, Harbin 150080, Heilongjiang, Peoples R China
来源：
CIS WORKSHOPS 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY WORKSHOPS | 2007年
关键词：
D O I：
10.1109/CIS.Workshops.2007.226
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) is easy to be influenced by selection of initial characters and converge to local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes and a model based on this method were presented in this paper. Compared with k-mean clustering RBF Algorithm, the result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.
引用
收藏
页码：59 / +
页数：3
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