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    This study introduces a novel asynchronous, dynamic event-triggered sliding mode control for Markov jump neural networks. The method ensures system synchronization efficiently by only sending control signals when necessary.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Network Systems

    Background:

    • Synchronization is crucial for Markov jump neural networks.
    • Existing control methods can be inefficient due to continuous signal transmission.

    Purpose of the Study:

    • To develop an efficient asynchronous and dynamic event-based sliding mode control strategy.
    • To address the synchronization problem in Markov jump neural networks.

    Main Methods:

    • A dynamic event-triggered scheme with an adaptive law and triggered threshold.
    • Design of an asynchronous sliding mode controller with gain uncertainty.
    • Utilization of linear matrix inequalities for synchronization conditions.

    Main Results:

    • Sufficient conditions for guaranteeing system synchronization are established.
    • Controller effectively pushes error system trajectories onto the sliding surface.
    • The proposed control strategy is validated through an illustrative example.

    Conclusions:

    • The asynchronous and dynamic event-based sliding mode control strategy is effective for synchronizing Markov jump neural networks.
    • The event-triggered approach reduces control signal transmission, enhancing efficiency.