On discrete-time cellular neural networks for associative memories

Abstract

In this paper, discrete-time cellular neural networks (DTCNNs) with a globally asymptotically stable equilibrium point, are designed to behave as associative memories. The objective is achieved by considering feedback parameters related to circulant matrices and by satisfying frequency domain stability criteria. The approach, by generating DTCNNs where the input data are fed via external inputs rather than initial conditions, enables both heteroassociative and autoassociative memories to be designed. Numerical examples are reported in order to show the capabilities of the proposed tool.