RBF Neural network based on ART neural network

被引：0

作者：

Meng, Xi ^{[1
]}

Qiao, Jun-Fei ^{[1
]}

Han, Hong-Gui ^{[1
]}

机构：

[1] College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing

来源：

Kongzhi yu Juece/Control and Decision | 2014年 / 29卷 / 10期

关键词：

Adaptive resonance theory(ART) network; Radial basis function(RBF) network; Structure design;

D O I：

10.13195/j.kzyjc.2013.0945

中图分类号：

学科分类号：

摘要：

For the problem that it is difficult to determine the hidden layer structure of the radial basis function(RBF) neural network, based on the good online classified characteristic of adaptive resonance theory(ART) neural network, a self-organizing RBF neural network structure design algorithm is proposed. The algorithm uses the clustering characteristic of ART neural network to design the RBF neural network structure. Through the similarity comparison of input vector, the number of the hidden layer nodes and initial parameters are determined, so that the network has simplified structure. The experiment results show that the proposed structure has a smaller number of nodes, fast learning speed and better approximation ability.

引用

页码：1876 / 1880

页数：4

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