Robust stability analysis for discrete-time uncertain neural networks with leakage time-varying delay

Author-Highlights
• This paper investigates robust stability problem for discrete-time neural network with leakage time-varying delay and parameter uncertainties.
• Based on Lyapunov Krasovskii functional method, sufficient conditions ensuring the stability of the networks are given in terms of linear matrix inequalities.
• Most updated techniques like reciprocal convex technique and double summation Jensen inequalities are utilized to obtain less conservative results.
• Numerical simulations are exploited to illustrate the applicability and conservatism of the developed theoretical results.

This paper is concerned with the stability problem for a class of discrete-time neural networks with time-varying delays in network coupling, parameter uncertainties and time-delay in the leakage term. By constructing triple Lyapunov–Krasovskii functional terms, based on Lyapunov method, new sufficient conditions are established to ensure the asymptotic stability of discrete-time delayed neural networks system. Convex reciprocal technique is incorporated to deal with double summation terms and the stability criteria are presented in terms of linear matrix inequalities (LMIs). Finally numerical examples are exploited to substantiate the theoretical results. It has also shown that the derived conditions are less conservative than the existing results in the literature.