Deqiang Zeng

TL;DR: This paper investigates the global asymptotic stability and stabilization of memristive neural networks (MNNs) with communication delays via event-triggered sampling control through a newly augmented Lyapunov-Krasovskii functional.

TL;DR: This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control and a novel Lyapunov–Krasovskii functional is constructed for synchronizing an error system.

TL;DR: An input-delay-dependent Lyapunov–Krasovskii functional with cubic function of input delay is successfully constructed, and the convex combination technique is used with ease to derive stability and stabilization criteria.

TL;DR: A novel Lyapunov–Krasovskii functional is constructed with some new augmented terms, which can fully capture the system characteristics and the available information on the actual sampling pattern, and ensures the master–slave synchronization of CLSs under a longer sampling period than remarkable existing works.

TL;DR: A new discontinuous Lyapunov–Krasovskii functional, namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is proposed for sampled-data systems for the first time, and a less conservative stabilization criterion is established.