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Two iterative reweighted algorithms for systems contaminated by outliers

Chen, Jing; Hu, Manfeng; Liu, Yanjun; Zhu, Quanmin

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Authors

Jing Chen

Manfeng Hu

Yanjun Liu

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Quan Zhu Quan.Zhu@uwe.ac.uk
Professor in Control Systems



Abstract

This study proposes two iterative reweighted (IRE) algorithms for systems whose data are contaminated by outliers. For the negative effect caused by the outliers, traditional least squares (LSs) and gradient descent (GD) algorithms cannot obtain unbiased estimates, while the variational Bayesian (VB) and expectation-maximization (EM) algorithms have the assumption that the prior knowledge of the outlier is available. To deal with these dilemmas, two IRE algorithms are developed. By assigning suitable weights for each dataset, unbiased parameter estimates can be obtained. In addition, the weights of the corrupted datasets become smaller and smaller with the increased number of iterations, and then, the contaminated data can be picked out from the datasets. The proposed algorithms do not require the prior knowledge of the outliers. Convergence analysis and numerical experiments show the effectiveness of the IRE algorithms.

Citation

Chen, J., Hu, M., Liu, Y., & Zhu, Q. (2023). Two iterative reweighted algorithms for systems contaminated by outliers. IEEE Transactions on Instrumentation and Measurement, 72, https://doi.org/10.1109/tim.2023.3308255

Journal Article Type Article
Acceptance Date Aug 8, 2023
Online Publication Date Aug 24, 2023
Publication Date Aug 24, 2023
Deposit Date Sep 30, 2023
Publicly Available Date Oct 4, 2023
Journal IEEE Transactions on Instrumentation and Measurement
Print ISSN 0018-9456
Electronic ISSN 1557-9662
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 72
DOI https://doi.org/10.1109/tim.2023.3308255
Keywords Electrical and Electronic Engineering, Instrumentation
Public URL https://uwe-repository.worktribe.com/output/11086291

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