New criteria on delay-dependent stability for discrete-time neural networks with time-varying delays

In this paper, the problem of delay-dependent stability for discrete-time neural networks with time-varying delays is investigated. By constructing a newly augmented Lyapunov-Krasovskii functional, a sufficient condition for guaranteeing the asymptotic stability of the concerned network is derived in the framework of linear matrix inequalities. Also, a further improved stability condition is developed by proposing a new activation condition which has not been considered in the literature. Two numerical examples are given to illustrate the effectiveness of the proposed methods.