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  Robust distributed estimation based on a generalized correntropy logarithmic difference algorithm over wireless sensor networks

Li, X., Feng, M., Chen, F., Shi, Q., Kurths, J. (2020): Robust distributed estimation based on a generalized correntropy logarithmic difference algorithm over wireless sensor networks. - Signal Processing, 177, 107731.
https://doi.org/10.1016/j.sigpro.2020.107731

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 Urheber:
Li, Xinyu1, Autor
Feng, Mingyu1, Autor
Chen, Feng1, Autor
Shi, Qing1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Distributed adaptive learning algorithms have played a critical role in signal processing and parameter estimation over networks. Most existing algorithms are based on the mean-square error (MSE) criterion, and they can achieve desirable performance when the noise is modeled as Gaussian. However, the performance of MSE-based algorithms may degrade dramatically with the impulsive noise. Therefore, the aim of this paper is to present a diffusion algorithm, named generalized correntropy-based logarithmic difference (d-GCLD) algorithm, for distributed estimation that incorporates robustness to wireless sensor networks (WSNs). By combining the logarithm operation and the correntropy criterion as the loss function, the proposed algorithm is robust to impulsive noise and achieves satisfactory performance in various situations. In addition, the stability problem is studied theoretically. Experimental results are given to demonstrate the validity of the new algorithm in different scenarios.

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 Datum: 2020-08-012020
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.sigpro.2020.107731
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Model / method: Machine Learning
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
 Art des Abschluß: -

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Titel: Signal Processing
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 177 Artikelnummer: 107731 Start- / Endseite: - Identifikator: Anderer: Elsevier
Anderer: 1872-7557
ISSN: 0165-1684
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/signal-processing
Publisher: Elsevier