A new global asymptotic stability result for delayed cellular neural networks

This paper studies the problem of global asymptotic stability for delayed cellular neural networks(DCNNs). A new stability condition is obtained by utilizing the Lyapunov functional method and the matrix inequality approach. This condition is less restrictive and generalizes some of the previous stability results derived in the literature.

1 INTRODUCTION

The study of the stability and equilibrium properties of delayed cellular neural networks(DCNNs) introduced in [1] has received much attention, see, for instance [1]–[13], where most of the results derive the conditions for the uniqueness and global asymptotic stability of the equilibrium point for DCNNs. The motivation for analyzing this type of stability for DCNNs is to solve some classes of optimization problems [1]–[4]. In the paper, we will specially refer to the references [10]–[13] as our new result basically which improves and generalizes the results given [10]–[13]. The mathematical model of a delayed cellular neural network is given by [1].