On the dynamic behavior of cellular neural networks

Abstract

Cellular neural networks (CNNs) are analog dynamic processors that have found several applications for the solution of complex computational problems. The mathematical model of a CNN consists in a large set of coupled nonlinear differential equations that have been mainly studied through numerical simulations; the knowledge of the dynamic behavior is essential for developing rigorous design methods and for establishing new applications. CNNs can be divided in two classes: stable CNNs, with the property that each trajectory (with the exception of a set of measure zero) converges towards an equilibrium point; unstable CNNs with either a periodic or a non/periodic (possibly complex) behavior. The paper is devoted to the comparison of the dynamic behavior of two CNN models: the original Chua-Yang model and the full range model, that was exploited for VLSI implementations.