DYPOF: Dynamically Adaptive RBF Neural Network with Potential Functions

Abstract

We present a method for data classification, which performs recognition based on a set of potential fields synthesized over the domain on input space by a number of potential function units. Proposed is DYPOF (DYnamic POtential Functions) neural network (NN), based on radial basis functions with a two-stage training procedure. A fundamental component in building DYPOF is a potential function entity (PFE), designed to generate a respective decision potential function. The desirable shape of the potential field characterizing the distribution of training set is synthesized by adjusting the weights as well as the parameter vectors of cumulative potential functions generated by the PFEs. The automatic adjustment of the minimum necessary number of hidden units-learning adjustment units (LAU)-for a given set of teaching patterns provides the network with a capability of performing dynamic adaptation and self-organization. We investigate the dependence of our method on these parameters and apply it to several data sets. The results indicate the power of the PFEs in generating classification solutions for various shapes of teaching patterns that are robust with respect to noise in the data.