Training RBF networks with perturbation methods

This paper describes a gradient descent technique for training radial basis function (RBF) networks which is suitable for hardware implementation. The method dynamically adjusts the positions and the widths of the basis functions so as to reduce the total output error of the network while the output connection weights are being trained. The algorithm is demonstrated by using it to train an RBF network to perform simple logical functions.