New Global Asymptotic Stability for Neural Networks with Time-Varying Discrete and Distributed Delays

Abstract

In this paper, the global asymptotic stability problem is considered for a class of neural networks with time-varying discrete and distributed delays. Based on the Lyapunov functional method, and by using the new technique for estimating the upper bound of the derivative of Lyapunov functional, the novel asymptotic stability criterion is derived in terms of linear matrix inequalities (LMIs). Two numerical examples are presented to show the less conservativeness of the proposed method.