SIMPLE AND ACCURATE CELL MACROMODELS FOR THE SIMULATIONS OF CELLULAR NEURAL NETWORKS

In this paper, two simple and accurate cell macromodels for PSPICE simulations of Cellular Neural Networks (CNNs) are designed. Firstly, a brief information about CNNs and their benefits are introduced. Then the nonlinear differential equations that characterize the CNNs and the equivalent cell circuit given by Chua and Yang which realizes these equations are presented. With appropriate source transformations, another cell equivalent that employs voltage controlled-voltage sources instead of voltage controlled-current sources is developed. By substituting the dependent sources with their actual circuits for both equivalents, complete systems which are suitable for PSPICE macromodeling are derived. Responses of astable and stable CNNs are analyzed with the proposed macromodels and satisfactory results are observed after the simulations. The benefits and drawbacks of the macromodels are also discussed in the conclusion section.