Journal article

Adaptive Learning for Robust Radial Basis Function Networks

Abd-Krim Seghouane, Navid Shokouhi

IEEE Transactions on Cybernetics | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2021


This article addresses the robust estimation of the output layer linear parameters in a radial basis function network (RBFN). A prominent method used to estimate the output layer parameters in an RBFN with the predetermined hidden layer parameters is the least-squares estimation, which is the maximum-likelihood (ML) solution in the specific case of the Gaussian noise. We highlight the connection between the ML estimation and minimizing the Kullback-Leibler (KL) divergence between the actual noise distribution and the assumed Gaussian noise. Based on this connection, a method is proposed using a variant of a generalized KL divergence, which is known to be more robust to outliers in the patter..

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University of Melbourne Researchers