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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Artificial neural networks (ANNs) are susceptible to bounded disturbances.
    • Reliable data transmission is crucial for accurate system observation.
    • Packet dropouts in communication channels can degrade decoding accuracy.

    Purpose of the Study:

    • To design a novel partial-neurons-based proportional-integral observer (PIO) for ANNs.
    • To address the challenges of bounded disturbances and packet dropouts in data transmission.
    • To improve the reliability and accuracy of estimation in ANNs.

    Main Methods:

    • Utilizing multiple description encoding for data transmission.
    • Modeling packet dropouts using Bernoulli-distributed stochastic variables.
    • Developing a PIO with parameters optimized for performance metrics.

    Main Results:

    • An explicit relationship quantifying the impact of packet dropouts on decoding accuracy was established.
    • A sufficient condition for assessing the boundedness of estimation error dynamics was provided.
    • PIO parameters were calculated via optimization for minimized ultimate bound and maximized decay rate.

    Conclusions:

    • The proposed PIO design strategy effectively handles bounded disturbances and packet dropouts in ANNs.
    • The method enhances estimation performance, ensuring reliable data processing.
    • The approach is validated through an illustrative example, demonstrating its applicability and advantages.