Recursive Parameter Estimation of Fractional Order Hammerstein Output Error Autoregressive Model.
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| Title: | Recursive Parameter Estimation of Fractional Order Hammerstein Output Error Autoregressive Model. |
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| Authors: | Li, Yanan1 (AUTHOR) 1938264040@qq.com, Li, Junhong1 (AUTHOR) missjunhong@163.com, Li, Fuchao1 (AUTHOR) 2212320017@stmail.ntu.edu.cn, Duan, Yaqi1 (AUTHOR) 221231006@stmail.ntu.edu.cn |
| Source: | Circuits, Systems & Signal Processing. Jul2025, Vol. 44 Issue 7, p4828-4846. 19p. |
| Subjects: | Polynomial operators, Parameter estimation, Separation (Technology), Algorithms |
| Abstract: | This paper studies the parameter identification problem of the fractional order Hammerstein output error autoregressive (FO-H-OEAR) model. First, the fractional order is introduced into the polynomial operator of the Hammerstein model. Then the key term separation technology is used in the model derivation process to obtain the FO-H-OEAR model. Second, this paper integrates the recursive technique into the maximum likelihood principle and proposes the fractional order maximum likelihood recursive least squares (ML-RLS) algorithm. Then the forgetting factor stochastic gradient (F-SG) algorithm is proposed as a comparative algorithm. Finally, two simulation examples are given. The results of the two simulation experiments show that the ML-RLS algorithm has higher accuracy and smaller identification error compared with the F-SG algorithm. Therefore, the ML-RLS algorithm can effectively estimate the parameters of the FO-H-OEAR model. [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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186308658 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Recursive Parameter Estimation of Fractional Order Hammerstein Output Error Autoregressive Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Yanan%22">Li, Yanan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1938264040@qq.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Junhong%22">Li, Junhong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> missjunhong@163.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Fuchao%22">Li, Fuchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2212320017@stmail.ntu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Duan%2C+Yaqi%22">Duan, Yaqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 221231006@stmail.ntu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. Jul2025, Vol. 44 Issue 7, p4828-4846. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polynomial+operators%22">Polynomial operators</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Separation+%28Technology%29%22">Separation (Technology)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper studies the parameter identification problem of the fractional order Hammerstein output error autoregressive (FO-H-OEAR) model. First, the fractional order is introduced into the polynomial operator of the Hammerstein model. Then the key term separation technology is used in the model derivation process to obtain the FO-H-OEAR model. Second, this paper integrates the recursive technique into the maximum likelihood principle and proposes the fractional order maximum likelihood recursive least squares (ML-RLS) algorithm. Then the forgetting factor stochastic gradient (F-SG) algorithm is proposed as a comparative algorithm. Finally, two simulation examples are given. The results of the two simulation experiments show that the ML-RLS algorithm has higher accuracy and smaller identification error compared with the F-SG algorithm. Therefore, the ML-RLS algorithm can effectively estimate the parameters of the FO-H-OEAR model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright 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/s00034-025-03016-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 4828 Subjects: – SubjectFull: Polynomial operators Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Separation (Technology) Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Recursive Parameter Estimation of Fractional Order Hammerstein Output Error Autoregressive Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Yanan – PersonEntity: Name: NameFull: Li, Junhong – PersonEntity: Name: NameFull: Li, Fuchao – PersonEntity: Name: NameFull: Duan, Yaqi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0278081X Numbering: – Type: volume Value: 44 – Type: issue Value: 7 Titles: – TitleFull: Circuits, Systems & Signal Processing Type: main |
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