Algorithms for Better Representation and Faster Learning in Radial Basis Function Networks

Abstract

In this paper we present upper bounds for the learning rates for hybrid models that employ a combination of both self-organized and supervised learning, using radial basis functions to build receptive field representations in the hidden units. The learning performance in such networks with nearest neighbor heuristic can be improved upon by multiplying the individual receptive field widths by a suitable overlap factor. We present results indicating optimal values for such overlap factors. We also present a new algorithm for determining receptive field centers. This method negotiates more hidden units in the regions of the input space as a function of the output and is conducive to better learning when the number of patterns (hidden units) is small.