View in EDS

Robust Stability Analysis for Discrete-Time Stochastic Neural Networks Systems with Impulses.

Saved in:
Bibliographic Details
Title: Robust Stability Analysis for Discrete-Time Stochastic Neural Networks Systems with Impulses.
Authors: Wu, Rui1 (AUTHOR) wurui_math@163.com, Cai, Ting1 (AUTHOR) caiting_math@163.com, Liu, Xin2 (AUTHOR) lxxinliu@163.com
Source: Circuits, Systems & Signal Processing. May2026, Vol. 45 Issue 5, p3513-3537. 25p.
Subjects: Robust stability analysis, State feedback (Feedback control systems), Exponential stability, Gene regulatory networks, Discrete-time systems, Control theory (Engineering), Artificial neural networks, Lyapunov functions
Abstract: This study investigates the problem of state feedback impulse robust stabilization and applies control techniques to linear discrete-time systems with impulse characteristics. Furthermore, it explores their extensions in the context of neural networks. The proposed framework is novel in its integration of impulsive dynamics, stochastic disturbances, and state feedback control within a unified discrete-time setting, significantly generalizing existing results that are limited to either continuous time or impulse free. The Lyapunov function is introduced, and sufficient conditions are derived to ensure robust exponential stability of the closed-loop system under state feedback control. A robust performance index is also defined to evaluate system performance. The findings are generalized from discrete-time stochastic impulsive systems to systems incorporating both state control and external disturbances. Finally, the effectiveness of the proposed methods is validated through a gene regulatory network model and a numerical example. [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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 194004473
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Robust Stability Analysis for Discrete-Time Stochastic Neural Networks Systems with Impulses.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Rui%22">Wu, Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wurui_math@163.com</i><br /><searchLink fieldCode="AR" term="%22Cai%2C+Ting%22">Cai, Ting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> caiting_math@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xin%22">Liu, Xin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lxxinliu@163.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. May2026, Vol. 45 Issue 5, p3513-3537. 25p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Robust+stability+analysis%22">Robust stability analysis</searchLink><br /><searchLink fieldCode="DE" term="%22State+feedback+%28Feedback+control+systems%29%22">State feedback (Feedback control systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Exponential+stability%22">Exponential stability</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+regulatory+networks%22">Gene regulatory networks</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete-time+systems%22">Discrete-time systems</searchLink><br /><searchLink fieldCode="DE" term="%22Control+theory+%28Engineering%29%22">Control theory (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+functions%22">Lyapunov functions</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study investigates the problem of state feedback impulse robust stabilization and applies control techniques to linear discrete-time systems with impulse characteristics. Furthermore, it explores their extensions in the context of neural networks. The proposed framework is novel in its integration of impulsive dynamics, stochastic disturbances, and state feedback control within a unified discrete-time setting, significantly generalizing existing results that are limited to either continuous time or impulse free. The Lyapunov function is introduced, and sufficient conditions are derived to ensure robust exponential stability of the closed-loop system under state feedback control. A robust performance index is also defined to evaluate system performance. The findings are generalized from discrete-time stochastic impulsive systems to systems incorporating both state control and external disturbances. Finally, the effectiveness of the proposed methods is validated through a gene regulatory network model and a numerical example. [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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194004473
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00034-025-03413-1
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 3513
    Subjects:
      – SubjectFull: Robust stability analysis
        Type: general
      – SubjectFull: State feedback (Feedback control systems)
        Type: general
      – SubjectFull: Exponential stability
        Type: general
      – SubjectFull: Gene regulatory networks
        Type: general
      – SubjectFull: Discrete-time systems
        Type: general
      – SubjectFull: Control theory (Engineering)
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Lyapunov functions
        Type: general
    Titles:
      – TitleFull: Robust Stability Analysis for Discrete-Time Stochastic Neural Networks Systems with Impulses.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wu, Rui
      – PersonEntity:
          Name:
            NameFull: Cai, Ting
      – PersonEntity:
          Name:
            NameFull: Liu, Xin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0278081X
          Numbering:
            – Type: volume
              Value: 45
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Circuits, Systems & Signal Processing
              Type: main
ResultId 1