Structure design for RBF neural network based on improved K-means algorithm

Abstract

Aiming at the disadvantages of the traditional K-means clustering algorithm, a new algorithm based on density is proposed to remove the noises and outliers in this paper. This algorithm determines whether a point is a noise or not according to the density of the point. Experiments show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the samples. Then the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm, meanwhile the number of cluster centers is gotten. The improved K-means algorithm is taken to optimize the structure of RBF neural network, and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability.