Fixed-time dual-channel event-triggered secure quasi-synchronization of coupled memristive neural networks

• Published in: Journal of the Franklin Institute Vol. 358; no. 18; pp. 10052 - 10078
• Main Authors: Bao, Yuangui, Zhang, Yijun
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.12.2021; Elsevier Science Ltd
• Subjects: Actuators; Controllers; Neural networks; Scheduling algorithms; Stochastic models; Time synchronization
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2021.10.023

This paper is concerned with the fixed-time quasi-synchronization of coupled memristive neural networks (CMNNs). The communication channel is subject to the deception attack described by the Bernoulli stochastic variable. To reduce signal transmissions, a dual-channel event-triggered mechanism is proposed. In each channel of sensor to controller and controller to actuator, an event-triggered mechanism is designed. Compared with the single event-triggered mechanism in the communication loop, the main difficulties lie in how to deal with the problems of packet scheduling and network attacks. By using Lyapunov method combining with a new proposed lemma, some sufficient conditions are derived to guarantee the leader-following quasi-synchronization of CMNNs. The Zeno behavior is excluded for the designed dual-channel event-triggered mechanism. The influence of the event-triggered mechanism on the estimation of settling time is discussed. Three numerical examples are provided to show the effectiveness of the theoretical results.