Modeling, analysis and design of a class of cellular neural networks

Abstract

In this paper modeling, analysis and design of a class of Cellular Neural Networks (CNNs) are discussed. In particular, a discrete-time CNN model is introduced and the global asymptotic stability of its equilibrium point is analyzed. By taking into account such stability results, a novel technique for designing associative memories is developed. The objective is achieved by satisfying frequency domain stability criteria via feedback parameters related to circulant matrices. The approach, by generating CNN's conditions, enables both hetero-associative and auto-associative memories to be designed. Finally, two examples highlight the capabilities of the designed networks in storing and retrieving information.