Second-order interpattern neural networks for optical pattern recognition

Abstract

By combining the autoassociative model with the heteroassociative model, we propose a second-order semi-complex IPA neural network. In this neural network, the real part or the imaginary part of a complete or partial complex vector not only addresses the corresponding part of the complex vector prestored in the neural network by autoassociation, but also addresses the corresponding imaginary or real part by heteroassociation. Owing to the introduction of the heteroassociation, the correct addressing probability is increased. Based on this neural network, a scheme for optical pattern recognition (OPR) is proposed. This scheme is composed of two cascaded optical systems; each performs a relative simple function. Compared with the matched spatial filter OPR system, the addressing results are direct and our scheme is feasible and promising.