Training RBF networks with selective backpropagation

Abstract

Backpropagation with selective training (BST) is applied on training radial basis function (RBF) networks. It improves the performance of the RBF network substantially, in terms of convergence speed and recognition error. Three drawbacks of the basic backpropagation algorithm, i.e. overtraining, slow convergence at the end of training, and inability to learn the last few percent of patterns are solved. In addition, it has the advantages of shortening training time (up to 3 times) and de-emphasizing overtrained patterns. The simulation results obtained on 16 datasets of the Farsi optical character recognition problem prove the advantages of the BST algorithm. Three activity functions for output cells are examined, and the sigmoid activity function is preferred over others, since it results in less sensitivity to learning parameters, faster convergence and lower recognition error.