Extended dissipativity analysis for discrete-time delayed neural networks based on an extended reciprocally convex matrix inequality

In this paper, the extended dissipativity analysis for discrete-time neural networks with a time-varying delay is investigated. First, a novel Lyapunov-Krasovskii functional (LKF) is constructed with a delay-product-type term introduced. Then, in the forward difference of the LKF, the sum terms are bounded via an extended reciprocally convex matrix inequality. As a result, an extended dissipativity criterion is established in terms of linear matrix inequalities. Meanwhile, this criterion is extended to the stability analysis of the counterpart system without disturbance. Finally, two numerical examples are given to demonstrate the effectiveness and improvements of the presented criterion. (C) 2018 Elsevier Inc. All rights reserved.