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    This study introduces an event-triggered mechanism for finite-time state estimation in stochastic neural networks with time delays. The proposed method enhances estimation accuracy while reducing data communication burdens.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Networked Systems

    Background:

    • State estimation is crucial for neural networks, but communication burdens and time delays pose challenges.
    • Existing methods often require continuous data transmission, leading to inefficiency.
    • Stochastic neural networks with mixed time delays require specialized estimation techniques.

    Purpose of the Study:

    • To develop an event-based finite-time state estimator for discrete-time stochastic neural networks.
    • To address challenges posed by mixed discrete and distributed time delays.
    • To reduce data communication load through an event-triggered mechanism.

    Main Methods:

    • A component-based event-triggered transmission mechanism was designed.
    • A new concept of finite-time boundedness in the mean square was introduced.
    • Stochastic analysis techniques were employed to establish sufficient conditions for performance.
    • An optimization problem was solved to determine the estimator gain matrix.

    Main Results:

    • The proposed event-based estimator ensures finite-time boundedness of the estimation error in the mean square.
    • The estimator effectively handles mixed time delays and external noise disturbances.
    • The settling-like time of the estimation error was minimized.
    • Numerical simulations validated the effectiveness of the proposed scheme.

    Conclusions:

    • The event-based approach significantly reduces communication requirements for state estimation.
    • The developed estimator provides guaranteed finite-time performance bounds.
    • This work offers a robust solution for state estimation in complex neural network systems.