Improving the Generalization Properties of Radial Basis Function Neural Networks

It is shown that a modification to the error functional allows smoothing to be introduced explicitly without significantly affecting the speed of training.

Abstract

An important feature of radial basis function neural networks is the existence of a fast, linear learning algorithm in a network capable of representing complex nonlinear mappings. Satisfactory generalization in these networks requires that the network mapping be sufficiently smooth. We show that a modification to the error functional allows smoothing to be introduced explicitly without significantly affecting the speed of training. A simple example is used to demonstrate the resulting improvement in the generalization properties of the network.