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Partial-Node-Based State Estimation for Delayed Complex Networks Under Intermittent Measurement Outliers: A

Lei Zou, Zidong Wang, Jun Hu

    IEEE Transactions on Neural Networks and Learning Systems
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    This study introduces a novel method for state estimation in delayed complex networks (DCNs) that effectively handles intermittent measurement outliers (IMOs). The new approach ensures reliable estimation by designing a multiple-order-holder (MOH) to resist outlier effects.

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    Area of Science:

    • Control Theory
    • Network Science
    • Signal Processing

    Background:

    • State estimation is crucial for understanding complex network dynamics.
    • Intermittent measurement outliers (IMOs) pose significant challenges to traditional estimation methods.
    • Existing methods often struggle with outliers exceeding norm-bounded noise limits.

    Purpose of the Study:

    • To develop a robust partial-node-based (PNB) state estimation scheme for delayed complex networks (DCNs) subject to IMOs.
    • To accurately model the intermittent nature and varying magnitudes of IMOs.
    • To ensure the exponential ultimate boundedness (EUB) of the state estimation error.

    Main Methods:

    • Modeling IMOs using shifted gate functions and parameterizing their frequency with minimum/maximum interval lengths.
    • Developing a novel multiple-order-holder (MOH) approach to process non-outlier measurements.
    • Constructing a PNB state estimator based on MOH outputs and solving an optimization problem for gain matrices.

    Main Results:

    • Sufficient conditions for the exponentially ultimate boundedness (EUB) of the estimation error were derived.
    • A novel outlier-resistant PNB state estimation scheme was successfully developed.
    • Simulation examples validated the effectiveness of the proposed outlier-resistant scheme.

    Conclusions:

    • The proposed MOH-based PNB state estimation method effectively mitigates the impact of IMOs in DCNs.
    • The developed conditions and optimization approach ensure robust and accurate state estimation.
    • This work provides a valuable tool for analyzing and controlling complex systems with unreliable measurements.