Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 34, Issue 10, Pages 7181-7195Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3138979
Keywords
State estimation; Complex networks; Technological innovation; Electronic mail; Weight measurement; Time measurement; Resists; Delayed complex networks (DCNs); intermittent measurement outliers (IMOs); multi-order-holder; partial-node-based (PNB) state estimation; ultimate boundedness
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This article focuses on the state estimation problem for delayed complex networks subject to intermittent measurement outliers. A novel multiple-order-holder approach is proposed to resist the effects of the outliers, and sufficient conditions for bounded estimation error are provided.
This article is concerned with the partial-node-based (PNB) state estimation problem for delayed complex networks (DCNs) subject to intermittent measurement outliers (IMOs). In order to describe the intermittent nature of outliers, several sequences of shifted gate functions are adopted to model the occurrence moments and the disappearing moments of IMOs. Two outlier-related indices, namely, minimum and maximum interval lengths, are employed to parameterize the ``occurrence frequency'' of IMOs. The norm of the addressed outlier is allowed to be greater than a certain fixed threshold, and this distinguishes the outlier from the extensively studied norm-bounded noise. By adopting the input-output models of the considered complex network, a novel multiple-order-holder (MOH) approach is developed to resist the effects of IMOs by dedicatedly designing a weighted average of certain non-IMO measurements, and then, a PNB state estimator is constructed based on the outputs of the MOHs. Sufficient conditions are proposed to ensure the exponentially ultimate boundedness (EUB) of the resultant estimation error, and the estimator gain matrices are subsequently obtained by solving a constrained optimization problem. Finally, two simulation examples are provided to demonstrate the effectiveness of our developed outlier-resistant PNB state estimation scheme.
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