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

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.