Hybrid Training Algorithm for RBF Network

This study presents a new hybrid algorithm for training RBF network. The algorithm consists of a proposed clustering algorithm to position the RBF centres and Givens least squares to estimate the weights. This paper begins with a discussion about the problems of clustering for positioning RBF centres. Then a clustering algorithm called moving k-means clustering algorithm was proposed to reduce the problems. The performance of the algorithm was then compared to adaptive k-means, non-adaptive k-means and fuzzy c-means clustering algorithms. Overall performance of the RBF network that used the proposed algorithm is much better than the ones that used other clustering algorithms. Simulation results also reveal that the algorithm is not sensitive to initial centres.