Adaptive Event-Triggered Space-Time Sampled-Data Synchronization for Fuzzy Coupling RDNNs Under Hybrid Random Cyber Attacks

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Abstract

This paper investigates the exponential synchronization of fuzzy coupling reaction-diffusion neural networks (RDNNs) under hybrid random cyber attacks. To efficaciously tolerate the cyber attacks and guarantee the expected performance for the proposed systems, a fuzzy-regulation-dependent adaptive spatiotemporal security sampled-data-based event-triggered control scheme (SDBETCS) is firstly introduced according to distinct fuzzy regulations. In light of the current and latest sampling signals, the threshold parameters can be timely and flexibly updated and the associated adaptive spatiotemporal SDBETCSs can be adaptively regulated for different fuzzy rules. In comparison with the conventional fuzzy SDBETCSs, the designed fuzzy adaptive spatiotemporal SDBETCS can not only reduce the event-triggering frequency but also effectively conserve more finite network communication resources. Through considering a discontinuous Lyapunov functional (LF), a new exponential synchronization criterion is provided for fuzzy coupling RDNNs. Furthermore, a more general fuzzy adaptive spatiotemporal SDBETCS with time-dependent and continuous threshold function is presented to compare with the traditional fuzzy SDBETCS. Lastly, demonstrative examples are given to verify the validity and feasibility of the theoretical analysis results and illustrate its potential application in image secure communication.
Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalIEEE Transactions on Fuzzy Systems
DOIs
Publication statusPublished - 19 Oct 2022

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