Abstract

Several modifications to parameter estimation in a Radial Basis Functions network are introduced. These include a better initializing clustering algorithm and a full gradient descent on centers and weights after weights were found via a matrix inversion. Performance comparison with other RBF algorithms is given on several data-sets. It is found that The proposed method was found superior to Bishop's EM training algorithm, to Orr's method [1] for as well as a conventional implementation. I. Introduction Radial basis functions have been extensively used for interpolation [2], [3], [4], [5], [6], [7] regression and classification due to their universal approximation properties and simple parameter estimation. The parameter estimation requires a (pseudo) inversion of a (possibly sparse) matrix. The possible numerical instability of the inversion (which is aggravated when the number of training patterns is small compared to the dimensionality) may be partially alleviated by further parame...

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