Event-triggered secure control for Markov jump neural networks with time-varying delays and subject to cyber-attacks via state estimation fuzzy approach

The investigation uses a state estimation fuzzy technique to address the problem of dynamic event-triggered secure control in Markov jump neural networks with time-varying delays subject to cyber-attacks. Two different deception attacks are given, each employing stochastic variables with a Bernoulli...

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Bibliographic Details
Published inInternational journal of systems science Vol. 56; no. 2; pp. 211 - 226
Main Authors Rajchakit, Grienggrai, Banu, K. Asmiya, Aparna, T., Lim, C. P.
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 25.01.2025
Taylor & Francis Ltd
Subjects
Communications traffic
cyber-attacks
Energy distribution
Event-triggered secure control
Fuzzy control
Fuzzy logic
Linear matrix inequalities
Markov jump
Neural networks
Stability criteria
State estimation
Time varying control
time-varying delay
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Summary:The investigation uses a state estimation fuzzy technique to address the problem of dynamic event-triggered secure control in Markov jump neural networks with time-varying delays subject to cyber-attacks. Two different deception attacks are given, each employing stochastic variables with a Bernoulli distribution. Specifically, this article presents a dynamic event-triggered strategy and energy restrictions to reduce network traffic and protect network resources during deception attacks. To prevent the system state from becoming unavailable, a state estimator is concurrently developed using the output signal. The application of Lyapunov stability theory yields sufficient criteria that ensure neural network stability. The study also accurately describes the controller gain's by solving Linear matrix inequality. Numerical examples are provided to demonstrate the theoretical results.
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content type line 14
ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2024.2390694