Construction of compact RBF network by refining coarse clusters and widths  

作　　者：Zeng Delu Zhou Zhiheng Xie Shengli 

机构地区：[1]School of Electronic and Information Engineering, South China Univ. of Technology, Guangzhou 510641, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2009年第6期1309-1315,共7页系统工程与电子技术（英文版）

基　　金：supported by Key Program of National Natural Science Foundation of China (U0635001);China Postdoctoral Science Foundation (20060390728);the Natural Science Fund of Guangdong Province, China (07006490)

摘　　要：It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function（RBF） network.This paper aims at constructing a compact RBF network with two main steps.In the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree（MDST）.Meanwhile the coarse widths are obtained.In the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse widths.Experiments of the proposed algorithm show its great performance in times series prediction and classification.It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function（RBF） network.This paper aims at constructing a compact RBF network with two main steps.In the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree（MDST）.Meanwhile the coarse widths are obtained.In the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse widths.Experiments of the proposed algorithm show its great performance in times series prediction and classification.

关 键 词：CLUSTERING anisotropic gradient descent radial basis function time series prediction boundary extraction. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O174.41[自动化与计算机技术—控制科学与工程]