New Globally Asymptotic Stability Criteria for Delayed Cellular Neural Networks

Abstract

This brief is concerned with the stability analysis for cellular neural networks with time-varying delays. First, an appropriate Lyapunov-Krasovskii functional is introduced to form some new delay-dependent stability conditions in terms of linear matrix inequalities (LMIs). Quite differently, these stability criteria are derived by using the convex combination property, which equivalently converts the original LMI containing a convex combination on the time-varying delay into two boundary LMIs. Second, this newly proposed approach is then extended to a class of uncertain neural networks with time-varying delays, from which new delay-dependent robust stability criteria are formulated. Finally, two numerical examples are given to show that the proposed criteria are of much less conservatism than the existing ones in the literature.