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Article: A New Variable Forgetting Factor-Based Bias-Compensation Algorithm for Recursive Identification of Time-Varying Multi-Input Single-Output Systems With Measurement Noise

TitleA New Variable Forgetting Factor-Based Bias-Compensation Algorithm for Recursive Identification of Time-Varying Multi-Input Single-Output Systems With Measurement Noise
Authors
KeywordsBias compensation
multi-input single-output (MISO) system
parameter estimation
recursive least-squares (RLS) algorithm
variable forgetting factor (VFF)
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=19
Citation
IEEE Transactions on Instrumentation and Measurement, 2020, v. 69 n. 7, p. 4555-4568 How to Cite?
AbstractThis article proposes a new variable forgetting factor (VFF) bias-compensated recursive least-squares (BCRLS) algorithm for the recursive identification of complex time-varying multi-input single-output (MISO) systems with measurement noise. It extends a previously developed real-valued BCRLS algorithm to complex signals and introduces new self-calibrated VFF and noise variance estimation schemes for tracking time-varying systems. The proposed VFF scheme offers faster tracking speed, especially for sudden system changes, while achieving a low steady-state (SS) mean square error (MSE) in a stationary environment. Moreover, the mean and mean square deviation of the complex RLS algorithm under zero-mean white Gaussian output additive noise are performed, from which the variance of the additive noise can be estimated. To mitigate the effect of finite-sample number, a self-calibration scheme is proposed to refine the FF at the SS and hence MSE. Simulations show that the proposed self-calibrated VFF-BCRLS algorithm offers improved tracking speed in sudden system changes and offers smaller MSE over the conventional BCRLS algorithm. Applications to real-world data for pH value prediction of a pH neutralization process and temperature prediction of a glass furnace also demonstrate the effectiveness of the proposed algorithm. The good performance and efficient implementation make it an attractive alternative to other conventional methods for system identification in control and optimization processes and other possible applications.
Persistent Identifierhttp://hdl.handle.net/10722/294065
ISSN
2019 Impact Factor: 3.658
2015 SCImago Journal Rankings: 1.022

 

DC FieldValueLanguage
dc.contributor.authorChan, SC-
dc.contributor.authorLIN, JQ-
dc.contributor.authorSun, X-
dc.contributor.authorTAN, HJ-
dc.contributor.authorXu, WC-
dc.date.accessioned2020-11-23T08:25:49Z-
dc.date.available2020-11-23T08:25:49Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement, 2020, v. 69 n. 7, p. 4555-4568-
dc.identifier.issn0018-9456-
dc.identifier.urihttp://hdl.handle.net/10722/294065-
dc.description.abstractThis article proposes a new variable forgetting factor (VFF) bias-compensated recursive least-squares (BCRLS) algorithm for the recursive identification of complex time-varying multi-input single-output (MISO) systems with measurement noise. It extends a previously developed real-valued BCRLS algorithm to complex signals and introduces new self-calibrated VFF and noise variance estimation schemes for tracking time-varying systems. The proposed VFF scheme offers faster tracking speed, especially for sudden system changes, while achieving a low steady-state (SS) mean square error (MSE) in a stationary environment. Moreover, the mean and mean square deviation of the complex RLS algorithm under zero-mean white Gaussian output additive noise are performed, from which the variance of the additive noise can be estimated. To mitigate the effect of finite-sample number, a self-calibration scheme is proposed to refine the FF at the SS and hence MSE. Simulations show that the proposed self-calibrated VFF-BCRLS algorithm offers improved tracking speed in sudden system changes and offers smaller MSE over the conventional BCRLS algorithm. Applications to real-world data for pH value prediction of a pH neutralization process and temperature prediction of a glass furnace also demonstrate the effectiveness of the proposed algorithm. The good performance and efficient implementation make it an attractive alternative to other conventional methods for system identification in control and optimization processes and other possible applications.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=19-
dc.relation.ispartofIEEE Transactions on Instrumentation and Measurement-
dc.rightsIEEE Transactions on Instrumentation and Measurement. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectBias compensation-
dc.subjectmulti-input single-output (MISO) system-
dc.subjectparameter estimation-
dc.subjectrecursive least-squares (RLS) algorithm-
dc.subjectvariable forgetting factor (VFF)-
dc.titleA New Variable Forgetting Factor-Based Bias-Compensation Algorithm for Recursive Identification of Time-Varying Multi-Input Single-Output Systems With Measurement Noise-
dc.typeArticle-
dc.identifier.emailChan, SC: scchan@eee.hku.hk-
dc.identifier.authorityChan, SC=rp00094-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIM.2019.2947121-
dc.identifier.scopuseid_2-s2.0-85087065517-
dc.identifier.hkuros319263-
dc.identifier.volume69-
dc.identifier.issue7-
dc.identifier.spage4555-
dc.identifier.epage4568-
dc.publisher.placeUnited States-

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