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Lmi Based Approach To Asymptotically Stability Analysis for Fractional Neutral-Type Neural Networks With Riemann Liouville Derivative

dc.authorscopusid 57194218825
dc.contributor.author Altun, Y.
dc.date.accessioned 2025-05-10T16:43:56Z
dc.date.available 2025-05-10T16:43:56Z
dc.date.issued 2022
dc.department T.C. Van Yüzüncü Yıl Üniversitesi en_US
dc.department-temp Altun Y., Department of Business Administration, Faculty of Management, Yuzuncu Yil University, Van, 65080, Turkey en_US
dc.description.abstract By this research paper, we search the asymptotically stability of fractional neutral-type neural networks with Riemann Liouville (RL) derivative. The activation functions discussed in this research are assumed to be globally Lipschitz continuous. The arguments of proposed stability requirements are based upon the linear matrix inequalities (LMIs) approach, which can be easily checked using the Lyapunov-Krasovskii functional. Finally, two simple examples and their simulations are presented to demonstrate that the obtained results are computationally flexible and effective © CSP - Cambridge, UK; I&S - Florida, USA, 2022 en_US
dc.identifier.endpage 647 en_US
dc.identifier.issn 1359-8678
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85131415782
dc.identifier.scopusquality Q4
dc.identifier.startpage 635 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14720/323
dc.identifier.volume 29 en_US
dc.identifier.wosquality N/A
dc.institutionauthor Altun, Y.
dc.language.iso en en_US
dc.publisher Cambridge Scientific Publishers en_US
dc.relation.ispartof Nonlinear Studies en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Asymptotically Stability en_US
dc.subject Fractional Neutral-Type Neural Networks en_US
dc.subject Lmi en_US
dc.subject Lyapunov-Krasovskii Functional en_US
dc.subject Rl Derivate en_US
dc.title Lmi Based Approach To Asymptotically Stability Analysis for Fractional Neutral-Type Neural Networks With Riemann Liouville Derivative en_US
dc.type Article en_US

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