Performance of a Digital Associative Memory Model for Pattern Recognition

Abstract

This article presents a digital associative memory (DAM) with pyramids of probabilistic logic nodes used as the basic processing element. The DAM can be applied to various pattern recognition systems or image classifiers. A reward/penalty error back propagation algorithm used to train the model will be described. Computer simulations are done to evaluate the performance of the model by training the network on associating a number of patterns from each class of the numerals 0–9 with their prototype model. The effect of the size of the training set on the convergence of the training algorithm is investigated.