Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays

This paper investigates the global asymptotic stability and stabilization of memristive neural networks (MNNs) with communication delays via event-triggered sampling control. First, based on the novel approach in Lemma 1, the concerned MNNs are converted into traditional neural networks with uncertain parameters. Next, a discrete event-triggered sampling control scheme, which only needs supervision of the system state at discrete instants, is designed for MNNs for the first time. Thanks to this controller, the number of control updates could greatly reduce. Then, by getting utmost out of the usable information on the actual sampling pattern, a newly augmented Lyapunov-Krasovskii functional (LKF) is constructed to formulate stability and stabilization criteria. It should be mentioned that the LKF is positive definite only at endpoints of each subinterval of the holding intervals but not necessarily positive definite inside the holding intervals. Finally, the feasibility and effectiveness of the proposed results are tested by two numerical examples.