Extended dissipativity analysis of semi-discretized multi-delayed memristive Cohen–Grossberg neural networks via non-fragile synchronization approach

Abstract

This paper deals with the extended dissipative performance of the discretized memristive Cohen–Grossberg neural networks (MCGNNs) together with multiple time-varying delays. At the outset, by employing the semi-discretization technique, the corresponding discrete-time analogue of MCGNNs is obtained, which preserves the dynamical behaviors of their continuous-time counterparts. Considering the fact that exogenous disturbances are inevitable in the network, the corresponding slave system is designed for the considered MCGNNNs along with the non-fragile state-feedback control. By employing the Lyapunov stability theory, the desired results ensuring the extended dissipativity of discretized MCGNNs are derived in terms of linear matrix inequalities. Further, numerical examples are illustrated to demonstrate the feasibility of the obtained dissipativity conditions of the semi discretized MCGNNs.