Learning algorithms for reformulated radial basis neural networks

Abstract

This paper proposes supervised learning algorithms based on gradient descent for training reformulated radial basis function (RBF) neural networks. Such RBF models employ radial basis functions whose form is determined by admissible generator functions. RBF networks with Gaussian radial basis functions are generated by exponential generator functions. A sensitivity analysis provides the basis for selecting generator functions by investigating the effect of linear, exponential and logarithmic generator functions on gradient descent learning. Experiments involving reformulated RBF networks indicate that the proposed gradient descent algorithms guarantee fast learning and very satisfactory function approximation capability.