An effective implementation for kernel‐based positive system identification using Gibbs sampling.
Saved in:
| Title: | An effective implementation for kernel‐based positive system identification using Gibbs sampling. |
|---|---|
| Authors: | Qiu, Chen1 (AUTHOR), Zheng, Man1 (AUTHOR) manzheng@ahu.edu.cn, Song, Jun1 (AUTHOR) |
| Source: | Asian Journal of Control. Mar2026, Vol. 28 Issue 2, p1039-1049. 11p. |
| Subjects: | Gibbs sampling, System identification, Gaussian distribution, Bayesian analysis, Regularization parameter, Markov chain Monte Carlo |
| Abstract: | Recently, the kernel‐based method has been applied for the positive system identification where the hyperparameter estimation is a crucial and critical part. The regularized identification problem for the positive system is first formulated. Due to the nonnegative constraint of the positive system, the Bayesian interpretation chooses the truncated Gaussian distribution as the prior of the system parameter. The hyperparameters estimation is cast as a marginal likelihood optimization. A Bayesian network for the positive system is established to construct a probabilistic framework for handling the hyperparameter estimation. Based on the derived conditional distributions, this paper develops a Markov chain Monte Carlo method to sample according to the likelihood function, where the Gibbs sampling is available to improve the sampling efficiency. The proposed sampling‐based algorithm facilitates searching for hyperparameters globally. The simulation results demonstrate that the proposed algorithm provides more precise and efficient identification compared to the conventional method. [ABSTRACT FROM AUTHOR] |
| Copyright of Asian Journal of 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 192204749 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: An effective implementation for kernel‐based positive system identification using Gibbs sampling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qiu%2C+Chen%22">Qiu, Chen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Man%22">Zheng, Man</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> manzheng@ahu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Song%2C+Jun%22">Song, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. Mar2026, Vol. 28 Issue 2, p1039-1049. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gibbs+sampling%22">Gibbs sampling</searchLink><br /><searchLink fieldCode="DE" term="%22System+identification%22">System identification</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution%22">Gaussian distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Regularization+parameter%22">Regularization parameter</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recently, the kernel‐based method has been applied for the positive system identification where the hyperparameter estimation is a crucial and critical part. The regularized identification problem for the positive system is first formulated. Due to the nonnegative constraint of the positive system, the Bayesian interpretation chooses the truncated Gaussian distribution as the prior of the system parameter. The hyperparameters estimation is cast as a marginal likelihood optimization. A Bayesian network for the positive system is established to construct a probabilistic framework for handling the hyperparameter estimation. Based on the derived conditional distributions, this paper develops a Markov chain Monte Carlo method to sample according to the likelihood function, where the Gibbs sampling is available to improve the sampling efficiency. The proposed sampling‐based algorithm facilitates searching for hyperparameters globally. The simulation results demonstrate that the proposed algorithm provides more precise and efficient identification compared to the conventional method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Asian Journal of 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192204749 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/asjc.3682 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1039 Subjects: – SubjectFull: Gibbs sampling Type: general – SubjectFull: System identification Type: general – SubjectFull: Gaussian distribution Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Regularization parameter Type: general – SubjectFull: Markov chain Monte Carlo Type: general Titles: – TitleFull: An effective implementation for kernel‐based positive system identification using Gibbs sampling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qiu, Chen – PersonEntity: Name: NameFull: Zheng, Man – PersonEntity: Name: NameFull: Song, Jun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15618625 Numbering: – Type: volume Value: 28 – Type: issue Value: 2 Titles: – TitleFull: Asian Journal of Control Type: main |
| ResultId | 1 |
