Accomodating Temporal Variations in Neural Networks

Abstract

Neural networks are very effective pattern classifiers, however, a major limitation is that they are unsuitable for classifying patterns with inherent time-variations. This paper describes an approach to incorporate a temporal structure in a neural network system which will accomodate the time variations in local feature sets encountered in problems such as partial shape classification.