Robust estimation for radial basis functions

This paper presents a new learning algorithm for radial basis functions (RBF) neural network, based on robust statistics. The extention of the learning vector quantizer for second order statistics is one of the classical approaches in estimating the parameters of a RBF model. The paper provides a comparative study for these two algorithms regarding their application in probability density function estimation. The theoretical bias in estimating one-dimensional Gaussian functions are derived. The efficiency of the algorithm is shown in modelling two-dimensional functions.