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Variance-Constrained State Estimation for Complex Networks With Randomly Varying Topologies.

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    This study develops a variance-constrained state estimator for complex networks with random topology changes and quantized measurements. The proposed method ensures estimation error bounds, demonstrating effectiveness via simulations.

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    Area of Science:

    • Control Theory
    • Network Science
    • Signal Processing

    Background:

    • Complex networks exhibit dynamic behaviors with randomly varying topologies and internal stochastic disturbances.
    • Measurement quantization introduces signal distortion, complicating accurate state estimation.
    • Existing methods often struggle with the combined challenges of stochasticity, topology variations, and quantization in nonlinear systems.

    Purpose of the Study:

    • To design a finite-horizon state estimator for nonlinear time-varying complex networks.
    • To guarantee prescribed variance constraints on estimation error despite stochastic inner coupling and measurement quantization.
    • To ensure desired performance requirements are met over a finite time horizon.

    Main Methods:

    • Utilizing Kronecker delta functions and Markovian jumping parameters for random topology changes.
    • Introducing Gaussian random variables to model stochastic disturbances in inner coupling.
    • Employing recursive linear matrix inequalities to establish sufficient conditions for estimator design.
    • Deriving estimator gain parameters based on the established conditions.

    Main Results:

    • Sufficient conditions for variance-constrained state estimation were successfully established.
    • The proposed estimator design algorithm guarantees performance bounds in the presence of network uncertainties.
    • Simulation results validated the effectiveness and applicability of the developed estimator.

    Conclusions:

    • The developed finite-horizon estimator effectively addresses state estimation challenges in complex networks with random topologies and quantized measurements.
    • The methodology provides a robust framework for guaranteeing estimation error performance under significant system uncertainties.
    • The study contributes a practical approach for reliable state estimation in advanced network systems.