Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach.
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| Title: | Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach. |
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| Authors: | Anh, Ho Pham Huy1,2 (AUTHOR) hphanh@hcmut.edu.vn, Dat, Nguyen Tien1,2 (AUTHOR) ntdat@hcmut.edu.vn |
| Source: | Neural Computing & Applications. Jun2026, Vol. 38 Issue 11, p1-24. 24p. |
| Abstract: | This study introduces a novel neural estimation method ensuring prescribed performance enabling the online identification of uncertain chaotic plants through which the transient-time and state residuals separately adjusted. The Lyapunov stability concept is applied for guaranteeing the stability in adaptive real-time system identification and control process. Outstanding from precedent researches, both identified scheme and training procedure along with control laws are initiatively designed as to permit separately adjusting the transient-time value from erroneous residual one. Then the real-time estimation process is to be satisfactorily investigated, in which the uncertain parts are parameterized using neural-based learning models, whose parameterized procedure helps efficiently solving more complicated problems, including adaptive observer and advanced adaptive neural-based control approaches. Here hyper-chaotic plants are comprehensively tested as to confirm the superiority and adaptability of proposed algorithm in comparison with other recently published advanced estimation approaches. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194192243 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Anh%2C+Ho+Pham+Huy%22">Anh, Ho Pham Huy</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hphanh@hcmut.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Dat%2C+Nguyen+Tien%22">Dat, Nguyen Tien</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ntdat@hcmut.edu.vn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2026, Vol. 38 Issue 11, p1-24. 24p. – Name: Abstract Label: Abstract Group: Ab Data: This study introduces a novel neural estimation method ensuring prescribed performance enabling the online identification of uncertain chaotic plants through which the transient-time and state residuals separately adjusted. The Lyapunov stability concept is applied for guaranteeing the stability in adaptive real-time system identification and control process. Outstanding from precedent researches, both identified scheme and training procedure along with control laws are initiatively designed as to permit separately adjusting the transient-time value from erroneous residual one. Then the real-time estimation process is to be satisfactorily investigated, in which the uncertain parts are parameterized using neural-based learning models, whose parameterized procedure helps efficiently solving more complicated problems, including adaptive observer and advanced adaptive neural-based control approaches. Here hyper-chaotic plants are comprehensively tested as to confirm the superiority and adaptability of proposed algorithm in comparison with other recently published advanced estimation approaches. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications 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/s00521-026-12152-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Titles: – TitleFull: Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Anh, Ho Pham Huy – PersonEntity: Name: NameFull: Dat, Nguyen Tien IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 38 – Type: issue Value: 11 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
