Self-improving associative neural network models

Abstract

A self-improving associative neural network (SIANN) model is presented. The implementation of this neural network consists of two phases, namely a learning procedure and a retrieval procedure. The learning procedure that determines connection weights among the neurons provides the ability to embody certain regularities implicit in a noisy pattern. It can be realized by a multilayer logic neural network using one pass. The self-improvement of the noisy pattern is achieved by the retrieval procedure. The salient points of the neural network model result from the fact that it does not require a set of training patterns, uses only one pass for the learning procedure, and converges very quickly. Computer experimental results illustrate the self-improvement of the neural network.<>