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Delay-Distribution-Dependent H

Hongjian Liu, Zidong Wang, Bo Shen

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    Summary
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    This study develops a state estimator for memristive neural networks with time-delays and fading measurements, ensuring stable estimation performance with H ∞ disturbance attenuation.

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    Area of Science:

    • Control Theory
    • Neural Networks
    • Signal Processing

    Background:

    • Memristive neural networks (MNNs) are crucial for complex computations.
    • State estimation in discrete-time MNNs is challenging due to time-delays and fading measurements.
    • Ensuring robust and stable state estimation is vital for MNN applications.

    Purpose of the Study:

    • To design a state estimator for discrete-time MNNs with mixed time-delays and fading measurements.
    • To guarantee stochastic stability of the estimation error dynamics.
    • To achieve a predefined H ∞ disturbance attenuation level.

    Main Methods:

    • Utilizing switching functions to model state-dependent memristive weights.
    • Applying robust analysis and Lyapunov-functional analysis theories.
    • Deriving sufficient conditions based on time-delay properties and Rice fading statistics.

    Main Results:

    • Sufficient conditions for guaranteed estimation performance were derived.
    • Gain matrices for the state estimator were obtained via matrix inequalities.
    • Numerical simulations validated the effectiveness of the proposed method.

    Conclusions:

    • The proposed state estimator design algorithm effectively addresses H ∞ state estimation for MNNs.
    • The derived conditions ensure robust estimation performance under complex network dynamics.
    • The method provides a practical approach for implementing state estimation in MNNs.