New existence and exponential stability results for periodic solutions in recurrent neural networks with generalized piecewise constant delay via coincidence degree theory.
Saved in:
| Title: | New existence and exponential stability results for periodic solutions in recurrent neural networks with generalized piecewise constant delay via coincidence degree theory. |
|---|---|
| Authors: | Chiu, Kuo-Shou1 kschiu@umce.cl |
| Source: | Mathematical Modelling & Analysis. 2026, Vol. 31 Issue 3, p476-498. 23p. |
| Subjects: | Recurrent neural networks, Exponential stability, Differential inequalities, Lyapunov functions, Limit cycles |
| Abstract: | The present work investigates recurrent neural systems incorporating generalized piecewise constant delay, with particular emphasis on establishing periodic behaviors and verifying their exponential convergence on a global scale. The existence of periodic solutions is established via Mawhin's coincidence degree in combination with sharp a priori estimates, while uniqueness and exponential attractivity are derived through a Lyapunov functional approach supported by differential inequalities adapted to the delay structure. The obtained criteria are concise, verifiable, and applicable in practice. Representative computational experiments are provided to substantiate the analytical findings. [ABSTRACT FROM AUTHOR] |
| Copyright of Mathematical Modelling & Analysis is the property of Vilnius Gediminas Technical University 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 | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194823834 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: New existence and exponential stability results for periodic solutions in recurrent neural networks with generalized piecewise constant delay via coincidence degree theory. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chiu%2C+Kuo-Shou%22">Chiu, Kuo-Shou</searchLink><relatesTo>1</relatesTo><i> kschiu@umce.cl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mathematical+Modelling+%26+Analysis%22">Mathematical Modelling & Analysis</searchLink>. 2026, Vol. 31 Issue 3, p476-498. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Exponential+stability%22">Exponential stability</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+inequalities%22">Differential inequalities</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+functions%22">Lyapunov functions</searchLink><br /><searchLink fieldCode="DE" term="%22Limit+cycles%22">Limit cycles</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The present work investigates recurrent neural systems incorporating generalized piecewise constant delay, with particular emphasis on establishing periodic behaviors and verifying their exponential convergence on a global scale. The existence of periodic solutions is established via Mawhin's coincidence degree in combination with sharp a priori estimates, while uniqueness and exponential attractivity are derived through a Lyapunov functional approach supported by differential inequalities adapted to the delay structure. The obtained criteria are concise, verifiable, and applicable in practice. Representative computational experiments are provided to substantiate the analytical findings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mathematical Modelling & Analysis is the property of Vilnius Gediminas Technical University 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=194823834 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3846/mma.2026.25296 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 476 Subjects: – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Exponential stability Type: general – SubjectFull: Differential inequalities Type: general – SubjectFull: Lyapunov functions Type: general – SubjectFull: Limit cycles Type: general Titles: – TitleFull: New existence and exponential stability results for periodic solutions in recurrent neural networks with generalized piecewise constant delay via coincidence degree theory. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chiu, Kuo-Shou IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13926292 Numbering: – Type: volume Value: 31 – Type: issue Value: 3 Titles: – TitleFull: Mathematical Modelling & Analysis Type: main |
| ResultId | 1 |
