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Neural network interval observer for nonlinear descriptor systems with the optimized hidden node distribution.

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Title: Neural network interval observer for nonlinear descriptor systems with the optimized hidden node distribution.
Authors: Liu, Zirui1 (AUTHOR), Huang, Jun1 (AUTHOR) cauchyhot@163.com, Sun, Yuan1 (AUTHOR)
Source: Journal of Vibration & Control. Jun2026, Vol. 32 Issue 11/12, p2914-2923. 10p.
Subjects: Radial basis functions, Descriptor systems, K-means clustering, Monotone operators, Artificial neural networks, Neural computers, Observability (Control theory)
Abstract: For nonlinear descriptor systems, this article presents a novel neural network interval observer. The framework of this interval observer is based on the combination of monotone systems theory and radial basis function neural networks, as the expansion of the definition of the general interval observer. Additionally, the K -means algorithm is used in the design process of the neural network interval observer to achieve the optimal distribution of hidden nodes, thus avoiding the drawbacks of traditional neural network methods. The number of hidden nodes is significantly decreased, and the persistence of excitation conditions is secured. Also, the interval estimation and convergence performance of the observer can be obtained. Eventually, the validity of the proposed method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Vibration & Control is the property of Sage Publications, Ltd. 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: 194090077
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Neural network interval observer for nonlinear descriptor systems with the optimized hidden node distribution.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zirui%22">Liu, Zirui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Jun%22">Huang, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cauchyhot@163.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Yuan%22">Sun, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptor+systems%22">Descriptor systems</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Monotone+operators%22">Monotone operators</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+computers%22">Neural computers</searchLink><br /><searchLink fieldCode="DE" term="%22Observability+%28Control+theory%29%22">Observability (Control theory)</searchLink>
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  Label: Abstract
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  Data: For nonlinear descriptor systems, this article presents a novel neural network interval observer. The framework of this interval observer is based on the combination of monotone systems theory and radial basis function neural networks, as the expansion of the definition of the general interval observer. Additionally, the K -means algorithm is used in the design process of the neural network interval observer to achieve the optimal distribution of hidden nodes, thus avoiding the drawbacks of traditional neural network methods. The number of hidden nodes is significantly decreased, and the persistence of excitation conditions is secured. Also, the interval estimation and convergence performance of the observer can be obtained. Eventually, the validity of the proposed method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Vibration & Control is the property of Sage Publications, Ltd. 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: 10.1177/10775463251337785
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 2914
    Subjects:
      – SubjectFull: Radial basis functions
        Type: general
      – SubjectFull: Descriptor systems
        Type: general
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Monotone operators
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Neural computers
        Type: general
      – SubjectFull: Observability (Control theory)
        Type: general
    Titles:
      – TitleFull: Neural network interval observer for nonlinear descriptor systems with the optimized hidden node distribution.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Liu, Zirui
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          Name:
            NameFull: Huang, Jun
      – PersonEntity:
          Name:
            NameFull: Sun, Yuan
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      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 10775463
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              Value: 32
            – Type: issue
              Value: 11/12
          Titles:
            – TitleFull: Journal of Vibration & Control
              Type: main
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