Exponential Inverse Square Root Recursive Spline Adaptive Filter for Nonlinear System Identification.
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| Title: | Exponential Inverse Square Root Recursive Spline Adaptive Filter for Nonlinear System Identification. |
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| Authors: | Chikyal, Neetu1,2 (AUTHOR) cn720055@student.nitw.ac.in, Vasundhara1 (AUTHOR) vasundhara@nitw.ac.in, Bhar, Chayan1 (AUTHOR) cbhar@nitw.ac.in |
| Source: | Circuits, Systems & Signal Processing. Sep2025, Vol. 44 Issue 9, p6352-6373. 22p. |
| Subjects: | Adaptive filters, Nonlinear estimation, Robust control, Random noise theory, Computer simulation |
| Abstract: | Lately, an adaptive exponential functional link network and its variants have been employed for nonlinear system identification. The nonlinear filters constructed upon spline exhibit efficacious nonlinear modelling along with lesser computations. However, non-Gaussian interference deteriorates the modelling ability of such nonlinear filters. Therefore, instead of this, the present manuscript intends to present a new exponential inverse square root recursive spline adaptive filter (EISSAF) for nonlinear system modelling. It incorporates a newly proposed exponential inverse square root cost criteria for imparting robustness to the proposed algorithm in the wake of non-Gaussian or impulsive noise contamination. The weight adaptation rule has been derived for the proposed method. Varied numerical simulations assert the potency of the introduced spline-dependent methodology in nonlinear system identification. [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: 187699735 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Exponential Inverse Square Root Recursive Spline Adaptive Filter for Nonlinear System Identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chikyal%2C+Neetu%22">Chikyal, Neetu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> cn720055@student.nitw.ac.in</i><br /><searchLink fieldCode="AR" term="%22Vasundhara%22">Vasundhara</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vasundhara@nitw.ac.in</i><br /><searchLink fieldCode="AR" term="%22Bhar%2C+Chayan%22">Bhar, Chayan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cbhar@nitw.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. Sep2025, Vol. 44 Issue 9, p6352-6373. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+estimation%22">Nonlinear estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Lately, an adaptive exponential functional link network and its variants have been employed for nonlinear system identification. The nonlinear filters constructed upon spline exhibit efficacious nonlinear modelling along with lesser computations. However, non-Gaussian interference deteriorates the modelling ability of such nonlinear filters. Therefore, instead of this, the present manuscript intends to present a new exponential inverse square root recursive spline adaptive filter (EISSAF) for nonlinear system modelling. It incorporates a newly proposed exponential inverse square root cost criteria for imparting robustness to the proposed algorithm in the wake of non-Gaussian or impulsive noise contamination. The weight adaptation rule has been derived for the proposed method. Varied numerical simulations assert the potency of the introduced spline-dependent methodology in nonlinear system identification. [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/s00034-025-03076-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 6352 Subjects: – SubjectFull: Adaptive filters Type: general – SubjectFull: Nonlinear estimation Type: general – SubjectFull: Robust control Type: general – SubjectFull: Random noise theory Type: general – SubjectFull: Computer simulation Type: general Titles: – TitleFull: Exponential Inverse Square Root Recursive Spline Adaptive Filter for Nonlinear System Identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chikyal, Neetu – PersonEntity: Name: NameFull: Vasundhara – PersonEntity: Name: NameFull: Bhar, Chayan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0278081X Numbering: – Type: volume Value: 44 – Type: issue Value: 9 Titles: – TitleFull: Circuits, Systems & Signal Processing Type: main |
| ResultId | 1 |
