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Chengda Lu, Min Wu, Yong He

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    Summary
    This summary is machine-generated.

    This study introduces a novel stubborn state estimator for delayed neural networks, effectively handling unbounded measurement disturbances like outliers. The adaptive saturation scheme ensures robust state estimation even with irregular and assorted noise.

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    Area of Science:

    • Control Systems Engineering
    • Neural Network Analysis
    • Signal Processing

    Background:

    • Delayed neural networks are susceptible to measurement disturbances, including outliers and impulsive noise.
    • Existing identification-based estimation methods struggle with unbounded, irregular, and assorted disturbances.

    Purpose of the Study:

    • To develop a robust state estimator for delayed neural networks capable of handling general measurement disturbances.
    • To design an adaptive saturation scheme to mitigate the impact of outliers and impulsive disturbances.

    Main Methods:

    • Construction of a stubborn state estimator with a saturation scheme on output estimation error injection.
    • Implementation of a dynamic saturation threshold governed by a designed equation for adaptiveness.
    • Utilization of a novel Lyapunov functional, generalized sector condition, and integral inequalities for stability analysis.

    Main Results:

    • A delay-dependent criterion for global stability of the estimation error system with dynamic saturation was derived.
    • A sufficient condition for co-designing the state estimator gain and saturation threshold evolution law was provided.
    • Numerical examples demonstrated the estimator's robustness against measurement outliers and impulsive disturbances.

    Conclusions:

    • The proposed adaptive saturation scheme effectively resists general measurement disturbances in delayed neural networks.
    • The developed state estimator exhibits stubbornness and improved performance in the presence of outliers and impulsive noise.