Adaptive accelerated proximal gradient algorithm for auto-regressive exogenous models with outliers
This study introduces an enhanced recursive least-squares algorithm that applies the adaptive accelerated proximal gradient method to identify Auto-Regressive Exogenous models with output outliers. First, the outlier problem was converted into a robust principal component analysis problem. The adapt...
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| Published in: | Applied mathematical modelling Vol. 133; pp. 310 - 326 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.09.2024
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| ISSN: | 0307-904X |
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| Abstract | This study introduces an enhanced recursive least-squares algorithm that applies the adaptive accelerated proximal gradient method to identify Auto-Regressive Exogenous models with output outliers. First, the outlier problem was converted into a robust principal component analysis problem. The adaptive accelerated proximal gradient method is then introduced to recover the information matrix, and the recursive least squares algorithm is applied to estimate the model parameters. Furthermore, we demonstrated that the parameter estimation is unbiased and that the parameter estimation error converges to zero under persistent excitation. A series of bench tests consistently highlighted the practicality and effectiveness of the proposed algorithm.
•The accelerated proximal gradient algorithm is introduced for effective outlier recovery.•The proposed adaptive accelerated proximal gradient algorithm can adaptively adjust the step-size.•A comprehensive framework is established by integrating accelerated proximal gradient and recursive least squares algorithms.•The proposed algorithm is validated through a state of charge estimation problem, demonstrating its effectiveness. |
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| AbstractList | This study introduces an enhanced recursive least-squares algorithm that applies the adaptive accelerated proximal gradient method to identify Auto-Regressive Exogenous models with output outliers. First, the outlier problem was converted into a robust principal component analysis problem. The adaptive accelerated proximal gradient method is then introduced to recover the information matrix, and the recursive least squares algorithm is applied to estimate the model parameters. Furthermore, we demonstrated that the parameter estimation is unbiased and that the parameter estimation error converges to zero under persistent excitation. A series of bench tests consistently highlighted the practicality and effectiveness of the proposed algorithm.
•The accelerated proximal gradient algorithm is introduced for effective outlier recovery.•The proposed adaptive accelerated proximal gradient algorithm can adaptively adjust the step-size.•A comprehensive framework is established by integrating accelerated proximal gradient and recursive least squares algorithms.•The proposed algorithm is validated through a state of charge estimation problem, demonstrating its effectiveness. |
| Author | Ji, Xixi Liu, Qiang Zhu, Quanmin Chen, Jing |
| Author_xml | – sequence: 1 givenname: Xixi surname: Ji fullname: Ji, Xixi email: xixiji19981107@126.com organization: School of Science, Jiangnan University, Wuxi 214122, PR China – sequence: 2 givenname: Jing orcidid: 0000-0001-5615-2255 surname: Chen fullname: Chen, Jing email: chenjing1981929@126.com organization: School of Science, Jiangnan University, Wuxi 214122, PR China – sequence: 3 givenname: Qiang surname: Liu fullname: Liu, Qiang email: minertech@qq.com organization: Minertech Technology Co.Ltd, Wuxi 214028, PR China – sequence: 4 givenname: Quanmin orcidid: 0000-0001-8173-1179 surname: Zhu fullname: Zhu, Quanmin email: quan.zhu@uwe.ac.uk organization: Department of Engineering Design and Mathematics, University of the West of England, Bristol BS16 1QY, UK |
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| Cites_doi | 10.1109/TSMCB.2012.2185490 10.1016/j.automatica.2020.109034 10.1137/070703983 10.1145/1970392.1970395 10.1016/j.jfranklin.2019.12.007 10.1016/j.automatica.2011.05.007 10.1016/j.sysconle.2018.03.003 10.1016/j.apm.2022.06.032 10.1007/s10851-010-0251-1 10.1109/TCYB.2016.2646059 10.1016/j.automatica.2021.109663 10.1002/rnc.6632 10.1109/TCSI.2022.3193444 10.1109/TPAMI.2003.1177153 10.1016/j.apm.2023.07.010 10.1109/TPEL.2011.2158554 10.1016/j.amc.2014.02.087 10.1109/87.974338 10.1007/s12555-019-0140-3 10.3390/math7050428 10.1016/j.apm.2007.07.003 10.1137/080716542 10.1145/1541880.1541882 10.1137/080738970 10.1016/j.sigpro.2017.02.006 |
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| Keywords | Adaptive accelerated proximal gradient Recursive identification Unbiased parameter estimate State of charge Outputs with outliers Convergence properties |
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| SubjectTerms | Adaptive accelerated proximal gradient Convergence properties Outputs with outliers Recursive identification State of charge Unbiased parameter estimate |
| Title | Adaptive accelerated proximal gradient algorithm for auto-regressive exogenous models with outliers |
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