Abstract: Owing to defects of lower classification precision and longer training time of Radial Basis Function Neural Network (RBF) classifier,a new self-adaptive clustering algorithm was produced firstly,which can be applied into construction of nodes in implicit layer．The new algorithm optimizes initial cluster centers by choosing good samples based on silhouette coefficients．It not only avoids the effects of initial centers in traditional k-means,but also avoids classification deviation．Secondly,the new algorithm was introduced into designing of RBF classifier．It can ascertain centers of radial basis function and its width efficiently．Finally,by a large number of tests and simulation,the new clustering algorithm was testified to be superior in clustering time,silhouette coefficients and accuracy rate．Besides,RBF classifier based on the advanced algorithm was proved to have higher precision.

Key words: Clustering,K-means,Radial basis function neural network