The Application of Dynamic K-means Clustering Algorithm in the Center Selection of RBF Neural Networks

Author

Liu, Hongyang ; He, Jia

Author_Institution

Coll. of Comput. Sci. & Technol., Chengdu Univ. of Inf. Technol., Chengdu, China

fYear

2009

fDate

14-17 Oct. 2009

Firstpage

488

Lastpage

491

Abstract

The key problem of constructing RBF neural networks is center selection. The method of adjusting the cluster centers is used in dynamic K-means clustering algorithm to make the choice of network-center more accurate. This paper, first introduced the structure of RBF Neural Networks (RBFNN) theory, and then applied the dynamic K-means clustering algorithm to the center selection of RBFNN. Our Simulation results show that the approximation of RBFNN, whose center selection is determined by the dynamic K-means clustering algorithm, has better performance and stronger practicality.

Keywords

pattern clustering; radial basis function networks; RBF Neural Networks; dynamic K-means clustering algorithm application; pattern recognition; radial basis function; Application software; Approximation algorithms; Clustering algorithms; Clustering methods; Computer applications; Computer networks; Function approximation; Genetics; Heuristic algorithms; Neural networks; Radial Basis Function; dynamic K-means clustering algorithm; function approximation; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on

Conference_Location

Guilin

Print_ISBN

978-0-7695-3899-0

Type

conf

DOI

10.1109/WGEC.2009.112

Filename

5402788