Open AccessExponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control
Author(s) -
Biwen Li,
Wenbo Zhou
Publication year - 2021
Publication title -
discrete dynamics in nature and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 39
eISSN - 1607-887X
pISSN - 1026-0226
DOI - 10.1155/2021/5575849
Subject(s) - control theory (sociology) , synchronization (alternating current) , controller (irrigation) , computer science , artificial neural network , zeno's paradoxes , event (particle physics) , exponential function , interval (graph theory) , lyapunov function , control (management) , mathematics , artificial intelligence , nonlinear system , physics , quantum mechanics , computer network , mathematical analysis , channel (broadcasting) , geometry , combinatorics , agronomy , biology
In this paper, we investigate the exponential synchronization problem of memristive neural networks (MNNs) with discrete and distributed time-varying delays under event-triggered control. An event-triggered controller with the static and dynamic event-triggering conditions is designed to improve the efficiency of resource utilization. By constructing a new Lyapunov function, some sufficient criteria are obtained to realize the exponential synchronization of considered drive-response MNNs under the designed event-triggered controller. In addition, the Zeno behavior will not occur by proving that the event-triggering interval has a positive lower bound under different event-triggering conditions. Finally, a numerical example is provided to prove the validity of our theoretical results.