Allahviranloo, T.Jafarian, A.Saneifard, R.Ghalami, N.Nia, S. MeasoomyKiani, F.Fernandez-Gamiz, U.2024-05-192024-05-1920231687-2770https://doi.org10.1186/s13661-023-01762-xhttps://hdl.handle.net/20.500.12713/5241This 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.eninfo:eu-repo/semantics/openAccessHigher-Order Linear Integro-Differential EquationArtificial Neural Network ApproachCaputo Fractional DerivativeLearning AlgorithmCost FunctionArticle20231WOS:0010293305000012-s2.0-85165256257N/A10.1186/s13661-023-01762-xQ3