Probabilistic Fuzzy ARTMAP: an autonomous neural network architecture for Bayesian probability estimation

Abstract

A hybrid utilisation of the Fuzzy ARTMAP (FAM) neural network and the Probabilistic Neural Network (PNN) is proposed for online learning and prediction tasks. FAM is used as an underlying clustering algorithm to classify the input patterns into different recognition categories during the learning phase. Subsequently, a non parametric probability estimation procedure in accordance with the PNN paradigm is employed during the prediction phase. This hybrid approach realises an incremental learning network with implementation of the Bayes strategy for online applications. The effectiveness of this network is assessed with statistical classification problems in both stationary and non stationary environments. Simulation studies illustrate that the network is capable of asymptotically approaching the Bayes optimal classification rates.