Global Asymptotic Stability of Delayed Cellular Neural Networks

A new criterion for the global asymptotic stability of the equilibrium point of cellular neural networks with multiple time delays is presented. The obtained result possesses the structure of a linear matrix inequality and can be solved efficiently using the recently developed interior-point algorithm. A numerical example is used to show the effectiveness of the obtained result

I. Introduction

Cellular neural networks have been successfully applied to signal processing, especially in image processing, solving nonlinear algebraic and transcendental equations and some classes of optimization problems. Some of these applications require that the equilibrium point of the designed cellular neural networks be unique and globally asymptotically stable. A number of criteria to achieve such a design have been proposed; see, for instance, [1]–[13], and the references cited therein.