CENTER SELECTION OF RBF NEURAL NETWORK BASED ON MODIFIED K-MEANS ALGORITHM WITH POINT SYMMETRY DISTANCE MEASURE

摘要

The hidden layer neurons of a radial basis function (RBF) neural network map input patterns from a nonlinearly separable space to a linearly separable space. To locate the centers of those hidden layer neurons, normally k-means clustering algorithm is used. Normal k-means clustering algorithm cannot detect hyper spherical-shaped clusters along the principal axes. In present study, we propose a modified version of the k-means clustering algorithm to select RBF centers, which can eliminate this drawback. In the proposed algorithm, we modify the k-means algorithm in two stages. In the first stage, the procedure to select the initial cluster centers has been modified to capture more knowledge about the distribution of input patterns. In the second stage, the initial centers, selected in the first stage are updated using point symmetry distance measure instead of using conventional Euclidean distance. The RBF neural network with the proposed algorithm has been tested with three different machine-learning data sets. It has also been applied for the segmentation of medical images. The experimental results show that the RBF neural network using the proposed modified k-means algorithm performs better than that using normal k-means algorithm.