Dynamic event-triggered synchronization control for hidden Markov jump neural networks subject to time-varying delay

This paper investigates the synchronization control problem of continuous hidden Markov jump neural networks (MJNNs) with time-varying delay (Td). Due to the challenge of accurately obtaining the system's mode information in real-world environments, the mode transitions of the original system a...

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Bibliographic Details
Published inIEEE access Vol. 13; p. 1
Main Authors Xu, WeiBing, Wang, Weichen, Su, Lei
Format Journal Article
LanguageEnglish
Published IEEE 2025
Subjects
Artificial neural networks
Biological neural networks
Control systems
Data communication
Delay effects
Delays
dynamic event-triggered mechanism
Event detection
Hidden Markov model (HMM)
Hidden Markov models
Markov jump neural networks
Neurons
Synchronization
synchronization control
time-varying delay
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Summary:This paper investigates the synchronization control problem of continuous hidden Markov jump neural networks (MJNNs) with time-varying delay (Td). Due to the challenge of accurately obtaining the system's mode information in real-world environments, the mode transitions of the original system are detected using a hidden Markov model. Subsequently, a controller is designed based on the detected mode information. To optimize the utilization of network resources and reduce the redundant data transmission, a dynamic event-triggered mechanism (DETM) is adopted. Then, through the application of Lyapunov stability theory, the delay-dependent Lyapunov functional is constructed and some sufficient conditions which ensure the system achieves stochastic stability and meet the H ∞ performance index η are established. Lastly, the feasibility of the method is validated through an example.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3599142