Non-fragile extended dissipative synchronization control of delayed uncertain discrete-time neural networks

Abstract

This article is concerned with the problem of robust extended dissipative synchronization for a class of delayed discrete-time neural networks through non-fragile state-feedback control. For this notion, we propose a new summation inequality based on extended reciprocally convex matrix inequality to linearize the quadratic summable terms induced through the finite-difference of the constructed Lyapunov–Krasovskii functional. By utilizing this derived inequality, the sufficient condition ensures the less conservative robust extended dissipativity performance is obtained in terms of linear matrix inequalities subject to both additive and multiplicative norm-bounded uncertainty. Finally, numerical examples are performed to acknowledge the significance and efficiency of the proposed theoretical results.