A Modified K-means Algorithm based RBF Neural Network and Its Application in Time Series Modelling

被引：0

作者：

Jiao, Yiping ^{[1
]}

Shen, Yu ^{[1
]}

Fei, Shumin ^{[1
]}

机构：

[1] Southeast Univ, Sch Automat, Nanjing, Jiangsu, Peoples R China

来源：

14TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS, ENGINEERING AND SCIENCE (DCABES 2015) | 2015年

关键词：

K-means Algorithm; Initial Cluster Centers; RBF Neural Network; Chaotic Time Series;

D O I：

10.1109/DCABES.2015.126

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, a modified K-means based RBFNN is discussed. To improve the performance of RBFNN, an initial cluster centers (ICCs) selection strategy is proposed for K-means algorithm. The algorithm takes breadth preferred subset of samples as ICCs to cover the sample space using greedy strategy. The results shows that the proposed algorithm can improve the performance of RBFNN remarkably in chaotic time series modelling.

引用

页码：481 / 484

页数：4

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