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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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A Neural-Network-Assisted Approach to Recursive State Estimation for Energy Harvesting Complex Networks With Unknown

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    This study develops a neural network-based recursive state estimation method for complex networks with energy harvesting sensors. The approach effectively estimates system states and unknown nonlinearities under energy constraints.

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    Area of Science:

    • Control Systems Engineering
    • Network Science
    • Signal Processing

    Background:

    • Complex networks (CNs) face challenges in state estimation due to unknown nonlinearities.
    • Energy harvesting sensors introduce intermittent data transmission, complicating estimation.
    • Partial node-based (PNB) recursive state estimation is crucial for monitoring these systems.

    Purpose of the Study:

    • To develop a robust recursive state estimation algorithm for CNs with unknown nonlinearities.
    • To address energy constraints imposed by energy harvesting sensors.
    • To simultaneously estimate system states and approximate unknown nonlinearities.

    Main Methods:

    • Utilized neural networks (NNs) for their universal approximation property to model unknown nonlinearities.
    • Developed an NN-based recursive estimation algorithm for simultaneous state and nonlinearity estimation.
    • Integrated an energy replenishment mechanism for sensors to manage transmission costs.

    Main Results:

    • Successfully generated estimates for both the system state and unknown nonlinearities.
    • Calculated recursive state estimator gains and NN weight (NNW) tuning parameters in a unified framework.
    • Demonstrated the algorithm's effectiveness through a simulation example.

    Conclusions:

    • The proposed NN-based recursive estimation algorithm effectively handles unknown nonlinearities and energy constraints in CNs.
    • The unified framework for calculating gains and tuning parameters offers an efficient approach.
    • The method provides a viable solution for state estimation in complex networks with energy harvesting sensors.