An analog network approach to train RBF networks based on sparse recovery

Abstract

The local competition algorithm (LCA) is an analog neural approach for compressed sensing. It is used to recover a sparse signal from a set of measurements. Unlike some traditional numerical methods that produce many elements with small magnitude, the LCA automatically set those unimportant elements to zero. This paper formulates the training process of radial basis function (RBF) networks as a compressed sensing problem. We then apply the LCA to train RBF networks. The proposed LCA-RBF approach can select important RBF nodes during training. Since the proposed approach can limit the magnitude of the trained weight, it also has certain ability to handle RBF networks with multiplicative weight noise.