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Robust Sparse Identification of Linear Parameter‐Varying Systems Using Variational Bayesian With Spike‐and‐Slab Prior.

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Title: Robust Sparse Identification of Linear Parameter‐Varying Systems Using Variational Bayesian With Spike‐and‐Slab Prior.
Authors: Chen, Xiang1 (AUTHOR), Li, Ke1,2 (AUTHOR), Wang, Zhiguo1 (AUTHOR) zhiguowang@jiangnan.edu.cn, Liu, Fei1 (AUTHOR)
Source: International Journal of Robust & Nonlinear Control. Jun2026, Vol. 36 Issue 9, p4859-4872. 14p.
Subjects: Bayesian analysis, Sparse approximations, Time-varying systems, Outlier detection, Parameter estimation, Model validation, Robust statistics
Abstract: This article considers the robust sparse identification problem of linear parameter‐varying (LPV) systems within the framework of the variational Bayesian (VB) algorithm. Considering that the order of the LPV model and the dependencies between the parameters and the scheduling variable are unknown, an over‐parameterization approach is adopted to construct the model representation. As a result, this leads to model redundancy and sparsity. To address this issue, the spike‐and‐slab (SS) prior is introduced to characterize the model parameters, enabling the acquisition of a sparse solution and thereby determining the true orders and structure of the LPV model. Subsequently, an auxiliary model is utilized to estimate the noise‐free output within the information vector. Additionally, a Student's t distribution is employed to handle outliers in the measured output, leading to a robust sparse identification algorithm. Finally, the proposed identification algorithm is effectively validated through a simulation example and the cascaded tank system. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Robust & Nonlinear Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193491444
AccessLevel: 6
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PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Robust Sparse Identification of Linear Parameter‐Varying Systems Using Variational Bayesian With Spike‐and‐Slab Prior.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Xiang%22">Chen, Xiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ke%22">Li, Ke</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhiguo%22">Wang, Zhiguo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhiguowang@jiangnan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Fei%22">Liu, Fei</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Robust+%26+Nonlinear+Control%22">International Journal of Robust & Nonlinear Control</searchLink>. Jun2026, Vol. 36 Issue 9, p4859-4872. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+approximations%22">Sparse approximations</searchLink><br /><searchLink fieldCode="DE" term="%22Time-varying+systems%22">Time-varying systems</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article considers the robust sparse identification problem of linear parameter‐varying (LPV) systems within the framework of the variational Bayesian (VB) algorithm. Considering that the order of the LPV model and the dependencies between the parameters and the scheduling variable are unknown, an over‐parameterization approach is adopted to construct the model representation. As a result, this leads to model redundancy and sparsity. To address this issue, the spike‐and‐slab (SS) prior is introduced to characterize the model parameters, enabling the acquisition of a sparse solution and thereby determining the true orders and structure of the LPV model. Subsequently, an auxiliary model is utilized to estimate the noise‐free output within the information vector. Additionally, a Student's t distribution is employed to handle outliers in the measured output, leading to a robust sparse identification algorithm. Finally, the proposed identification algorithm is effectively validated through a simulation example and the cascaded tank system. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Robust & Nonlinear Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/rnc.70440
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 4859
    Subjects:
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Sparse approximations
        Type: general
      – SubjectFull: Time-varying systems
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Model validation
        Type: general
      – SubjectFull: Robust statistics
        Type: general
    Titles:
      – TitleFull: Robust Sparse Identification of Linear Parameter‐Varying Systems Using Variational Bayesian With Spike‐and‐Slab Prior.
        Type: main
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          Name:
            NameFull: Chen, Xiang
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            NameFull: Li, Ke
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          Name:
            NameFull: Wang, Zhiguo
      – PersonEntity:
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            NameFull: Liu, Fei
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 36
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              Value: 9
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            – TitleFull: International Journal of Robust & Nonlinear Control
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