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S-asymptotically (ω, c)-periodic behavior of hybrid-time shunting inhibitory cellular neural networks with delays and stochastic perturbations.

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Title: S-asymptotically (ω, c)-periodic behavior of hybrid-time shunting inhibitory cellular neural networks with delays and stochastic perturbations.
Authors: Bharti, Puja1 (AUTHOR) pujab@rgipt.ac.in, Dhama, Soniya1 (AUTHOR) soniyad@rgipt.ac.in
Source: Applied Mathematics & Computation. Oct2026, Vol. 526, pN.PAG-N.PAG. 1p.
Subjects: Stochastic processes, Cellular neural networks (Computer science), Numerical analysis, Exponential stability, Artificial neural networks
Abstract: This paper investigates pth -mean S -asymptotically (ω, c)-periodic stochastic processes on periodic time scales and applies it to stochastic shunting-inhibitory cellular neural networks characterized by discrete time-varying delays and infinite distributed delays. In the presence of standard Lipschitz and growth conditions, as well as stochastic perturbations driven by a Wiener process, we examine the existence and uniqueness (pathwise) of pth -mean S -asymptotically (ω, c)-periodic solutions. Furthermore, we derive sufficient conditions for their pth -mean exponential stability. The analysis makes use of time scale calculus, a Banach space setup that is appropriate for the (ω, c)-weighted processes, fixed-point arguments, and estimates of the Burkholder-Davis-Gundy type inequality for the stochastic integrals on time scales. The results of the theoretical analysis are illustrated and validated through the use of numerical simulations on representative continuous, discontinuous, and nonuniform time scales. [ABSTRACT FROM AUTHOR]
Copyright of Applied Mathematics & Computation is the property of Elsevier B.V. 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.)
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An: 193369036
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  Data: S-asymptotically (ω, c)-periodic behavior of hybrid-time shunting inhibitory cellular neural networks with delays and stochastic perturbations.
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  Data: <searchLink fieldCode="JN" term="%22Applied+Mathematics+%26+Computation%22">Applied Mathematics & Computation</searchLink>. Oct2026, Vol. 526, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+neural+networks+%28Computer+science%29%22">Cellular neural networks (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Exponential+stability%22">Exponential stability</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
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  Label: Abstract
  Group: Ab
  Data: This paper investigates pth -mean S -asymptotically (ω, c)-periodic stochastic processes on periodic time scales and applies it to stochastic shunting-inhibitory cellular neural networks characterized by discrete time-varying delays and infinite distributed delays. In the presence of standard Lipschitz and growth conditions, as well as stochastic perturbations driven by a Wiener process, we examine the existence and uniqueness (pathwise) of pth -mean S -asymptotically (ω, c)-periodic solutions. Furthermore, we derive sufficient conditions for their pth -mean exponential stability. The analysis makes use of time scale calculus, a Banach space setup that is appropriate for the (ω, c)-weighted processes, fixed-point arguments, and estimates of the Burkholder-Davis-Gundy type inequality for the stochastic integrals on time scales. The results of the theoretical analysis are illustrated and validated through the use of numerical simulations on representative continuous, discontinuous, and nonuniform time scales. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Mathematics & Computation is the property of Elsevier B.V. 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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      – Type: doi
        Value: 10.1016/j.amc.2026.130071
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Cellular neural networks (Computer science)
        Type: general
      – SubjectFull: Numerical analysis
        Type: general
      – SubjectFull: Exponential stability
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      – SubjectFull: Artificial neural networks
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      – TitleFull: S-asymptotically (ω, c)-periodic behavior of hybrid-time shunting inhibitory cellular neural networks with delays and stochastic perturbations.
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              Text: Oct2026
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              Y: 2026
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