Improved delay-dependent stability analysis of discrete-time neural networks with time-varying delay

This paper presents two improved delay-dependent stability criteria for discrete-time neural networks with time-varying delay. First, a Lyapunov-Krasovskii functional (LKF) with several augmented terms is constructed. Then an improved summation inequality, together with Wirtinger-based inequality, is employed to give tight estimations for sum terms in the forward difference of the LKF. Moreover, two methods for handling the time-varying delay information are applied. As a result, two stability criteria in terms of linear matrix inequality are established. Finally, two numerical examples are given to demonstrate the effectiveness and benefits of the developed stability criteria.