CONDITIONAL CONVERGENCE OF PHOTOREFRACTIVE PERCEPTRON LEARNING

Abstract

We consider the convergence characteristics of a perceptron learning algorithm, taking into account the decay of photorefractive holograms during the process of interconnection weight changes. As a result of the hologram erasure, the convergence of the learning process is dependent on the exposure time during the weight changes. A mathematical proof of the conditional convergence, as well as computer simulations of the photorefractive perceptrons, is presented and discussed.