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

Authors:
Jia He;Hongyang Liu
Affiliations:
-;-
Venue:
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Year:
2009

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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.