An application of artificial neural networks for solving fractional higher-order linear integro-differential equations

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dc.authoridNoeiaghdam, Samad/0000-0002-2307-0891
dc.authoridAllahviranloo, Tofigh/0000-0002-6673-3560
dc.authoridKiani, Farzad/0000-0002-0354-9344
dc.authorwosidNoeiaghdam, Samad/N-8476-2016
dc.authorwosidAllahviranloo, Tofigh/V-4843-2019
dc.authorwosidKiani, Farzad/O-3363-2013
dc.contributor.authorAllahviranloo, T.
dc.contributor.authorJafarian, A.
dc.contributor.authorSaneifard, R.
dc.contributor.authorGhalami, N.
dc.contributor.authorNia, S. Measoomy
dc.contributor.authorKiani, F.
dc.contributor.authorFernandez-Gamiz, U.
dc.date.accessioned2024-05-19T14:42:26Z
dc.date.available2024-05-19T14:42:26Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis ongoing work is vehemently dedicated to the investigation of a class of ordinary linear Volterra type integro-differential equations with fractional order in numerical mode. By replacing the unknown function by an appropriate multilayered feed-forward type neural structure, the fractional problem of such initial value is changed into a course of non-linear minimization equations, to some extent. Put differently, interest was sparked in structuring an optimized iterative first-order algorithm to estimate solutions for the origin fractional problem. On top of that, some computer simulation models exemplify the preciseness and well-functioning of the indicated iterative technique. The outstanding accomplished numerical outcomes conveniently reflect the productivity and competency of artificial neural network methods compared to customary approaches.en_US
dc.identifier.doi10.1186/s13661-023-01762-x
dc.identifier.issn1687-2770
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85165256257en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org10.1186/s13661-023-01762-x
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5241
dc.identifier.volume2023en_US
dc.identifier.wosWOS:001029330500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofBoundary Value Problemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectHigher-Order Linear Integro-Differential Equationen_US
dc.subjectArtificial Neural Network Approachen_US
dc.subjectCaputo Fractional Derivativeen_US
dc.subjectLearning Algorithmen_US
dc.subjectCost Functionen_US
dc.titleAn application of artificial neural networks for solving fractional higher-order linear integro-differential equationsen_US
dc.typeArticleen_US

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