Auxiliary Variable-Based Identification Algorithms for Uncertain-Input Models

This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive l...

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Vydané v:	Circuits, systems, and signal processing Ročník 39; číslo 7; s. 3389 - 3404
Hlavní autori:	Chen, Jing, Zhu, Quanmin, Chandra, Budi, Pu, Yan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2020 Springer Nature B.V
Predmet:	Algorithms Circuits and Systems Computer simulation Electrical Engineering Electronics and Microelectronics Engineering Instrumentation Least squares Parameter estimation Signal,Image and Speech Processing Unbiased parameter estimate Uncertain-input model Computational effort Auxiliary variable Least squares algorithm
ISSN:	0278-081X, 1531-5878
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Abstract	This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive least squares algorithm is proposed to reduce the computational efforts. To validate the framework and algorithms developed, it has conducted a series of bench tests with computational experiments. The simulated numerical results/plots are consistent with the analytically derived results in terms of the feasibility and effectiveness of the proposed procedure.
AbstractList	This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive least squares algorithm is proposed to reduce the computational efforts. To validate the framework and algorithms developed, it has conducted a series of bench tests with computational experiments. The simulated numerical results/plots are consistent with the analytically derived results in terms of the feasibility and effectiveness of the proposed procedure.
Author	Pu, Yan Chandra, Budi Zhu, Quanmin Chen, Jing
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Cites_doi	10.1109/TNNLS.2019.2904952 10.1016/j.cam.2018.06.016 10.1109/TII.2017.2712743 10.1109/TII.2019.2931792 10.1016/j.jfranklin.2018.08.013 10.1016/j.automatica.2015.05.001 10.1016/j.sigpro.2016.05.009 10.1007/s00034-018-0998-y 10.1002/aic.14147 10.1016/j.automatica.2018.04.003 10.1109/TCYB.2019.2900469 10.1016/j.automatica.2019.03.014 10.1007/s00034-018-1015-1 10.1016/j.apm.2017.10.005 10.1017/CBO9781139020411 10.1049/iet-cta.2018.5411 10.1016/j.sysconle.2012.02.009 10.1016/j.automatica.2005.05.008 10.1007/s11071-014-1771-9 10.1016/j.sysconle.2013.05.012 10.1007/s00034-018-0871-z 10.1016/j.engappai.2019.04.009 10.1109/TCST.2014.2387216 10.1007/s00521-019-04204-x 10.1109/TSMC.2017.2689920 10.1109/TCYB.2016.2646059 10.1016/j.automatica.2017.01.016 10.1016/j.cam.2019.01.022 10.1007/s00034-016-0333-4 10.1016/j.automatica.2006.11.025
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Snippet	This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can...
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SubjectTerms	Algorithms Circuits and Systems Computer simulation Electrical Engineering Electronics and Microelectronics Engineering Instrumentation Least squares Parameter estimation Signal,Image and Speech Processing
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Title	Auxiliary Variable-Based Identification Algorithms for Uncertain-Input Models
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