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.
['Jia He', 'Hongyang Liu']
The Application of Dynamic K-means Clustering Algorithm in the Center Selection of RBF Neural Networks