Dynamic Event-Triggered Synchronization Control for Hidden Markov Jump Neural Networks Subject to Time-Varying Delay

• Published in: IEEE access Vol. 13; pp. 146569 - 146578
• Main Authors: Xu, Weibing, Wang, Weichen, Su, Lei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biological neural networks; Control systems; Data communication; Data transmission; Delay; Delay effects; Delays; dynamic event-triggered mechanism; Event detection; Hidden Markov model (HMM); Hidden Markov models; Markov chains; Markov jump neural networks; Neural networks; Neurons; Performance indices; Stability; Synchronism; Synchronization; synchronization control; Time varying control; time-varying delay
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3599142

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 <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> performance index <inline-formula> <tex-math notation="LaTeX">\eta </tex-math></inline-formula> are established. Lastly, the feasibility of the method is validated through an example.