Fixed-time synchronization of coupled memristive neural networks via event-triggered control

Yuangui Bao, Yijun Zhang and Baoyong Zhang

Applied Mathematics and Computation, 2021, vol. 411, issue C

Abstract: This paper is concerned with the fixed-time synchronization of coupled memristive neural networks (MNNs). To reduce the frequency of controller update, an event-triggered control approach is introduced. For this synchronization problem, the main difficulty lies in how to launch the fixed-time analysis in the parameter unmatching conditions and with event-triggered schemes. For the case when each follower in coupled MNNs have direct access to the leader MNN, a decentralized event-triggered scheme is provided. A distributed event-triggered controller is also designed when the node in coupled MNNs can only get the information of its neighbors. Some sufficient criteria are derived to guarantee the fixed-time event-triggered synchronization of considered coupled MNNs under these two situations, respectively. The upper bounds of the settling time are given and the Zeno behaviour is excluded for the two cases. Two numerical examples are provided to show the effectiveness of the obtained results. A potential application in secure communication is further discussed.

Keywords: Fixed-time; Synchronization; Event-triggered control; Coupled memristive neural networks (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:411:y:2021:i:c:s0096300321006263

DOI: 10.1016/j.amc.2021.126542

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