Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations

This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communica...

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Bibliographic Details
Published inChinese physics B Vol. 33; no. 7; pp. 70203 - 135
Main Authors Shao, Xiao-Guang, Zhang, Jie, Lu, Yan-Juan
Format Journal Article
LanguageEnglish
Published Chinese Physical Society and IOP Publishing Ltd 01.06.2024
Subjects
dynamic event-triggered mechanism
memristor-based neural networks
proportional delays
sensor saturations
sen-sor saturations
memristor-based neural networks
proportional delays
dynamic event-triggered mechanism
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ISSN1674-1056
2058-3834
DOI10.1088/1674-1056/ad3dcb

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Summary:This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism, instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.
ISSN:1674-1056
2058-3834
DOI:10.1088/1674-1056/ad3dcb