Academic Journal
Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations.
| Τίτλος: | Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations. |
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| Συγγραφείς: | Xu, Ning, Liu, Qinyao, Ding, Feng |
| Πηγή: | Circuits, Systems & Signal Processing; Nov2024, Vol. 43 Issue 11, p7089-7116, 28p |
| Θεματικοί όροι: | Autoregressive models, Time-varying systems, Parameter estimation, Parameter identification, Algorithms |
| Περίληψη: | It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s00034-024-02776-1 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xu%2C+Ning%22">Xu, Ning</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qinyao%22">Liu, Qinyao</searchLink><br /><searchLink fieldCode="AR" term="%22Ding%2C+Feng%22">Ding, Feng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Circuits, Systems & Signal Processing; Nov2024, Vol. 43 Issue 11, p7089-7116, 28p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Autoregressive+models%22">Autoregressive models</searchLink><br /><searchLink fieldCode="DE" term="%22Time-varying+systems%22">Time-varying systems</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+identification%22">Parameter identification</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature 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: 10.1007/s00034-024-02776-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 7089 Subjects: – SubjectFull: Autoregressive models Type: general – SubjectFull: Time-varying systems Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Parameter identification Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Gradient-Based Recursive Parameter Estimation Methods for a Class of Time-Varying Systems from Noisy Observations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xu, Ning – PersonEntity: Name: NameFull: Liu, Qinyao – PersonEntity: Name: NameFull: Ding, Feng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 0278081X Numbering: – Type: volume Value: 43 – Type: issue Value: 11 Titles: – TitleFull: Circuits, Systems & Signal Processing Type: main |
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