Improved k-means algorithm in the design of RBF neural networks

We propose an improved version of the normal k-means clustering algorithm to select the hidden layer neurons of a radial basis function (RBF) neural network. The normal k-means algorithm has been modified to capture more knowledge about the distribution of input patterns and to take care of hyper-ellipsoidal shaped clusters. The RBF neural network with the proposed algorithm has been tested with three different machine-learning data sets. The average recognition rate of an RBF neural network over these data sets has been found to be 93.70% using the proposed improved k-means algorithm, whereas in the method using the normal k-means algorithm, the corresponding value is found to be 88.12%. Clearly, the results show that the performance of the RBF neural network using the proposed modified k-means algorithm has been improved.