<title>Associative storage of multiple image patterns in a feedback neural network</title>

Abstract

A feedback neural network (FBNN) can be triggered by ANY input analog pattern vector. Then depending on the domain-of-convergence (or domain-of-attraction in the languages of nonlinear systems) that this triggering pattern falls into, the FBNN will go around and around the feedback loop and finally settle down at one of the few designated patterns associatively stored in the connection matrix. This recalled (or the settle-down) pattern will stay at the output even when the input triggering pattern is removed because of the self-sustained feedback action of the FBNN. The triggering pattern does not have to be the same as the stored pattern that it recalls. It can be a noise-affected pattern. But as long as it falls within the designated noise range (or the designated domain of convergence) of an accurately stored pattern, that accurate pattern will be recalled and permanently appear at the output even when the input triggering is removed. This paper derives, from the principle of NONITERATIVE LEARNING, the basic design method of this FBNN with controlled domains-of-convergence taken into account in the design.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.