Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time-Varying Delay

Abstract

The problem of global exponential stability for recurrent neural networks with time-varying delay is investigated. By dividing the time delay interval [$0,\tau (t)$] into $K+1$ dynamical subintervals, a new Lyapunov-Krasovskii functional is introduced; then, a novel linear-matrix-inequality (LMI-) based delay-dependent exponential stability criterion is derived, which is less conservative than some previous literatures (Zhang et al., 2005; He et al., 2006; and Wu et al., 2008). An illustrate example is finally provided to show the effectiveness and the advantage of the proposed result.