Application of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations

dc.contributor.authorKhan, Naveed Ahmad
dc.contributor.authorIbrahim Khalaf, Osamah
dc.contributor.authorTavera Romero, Carlos Andrés
dc.contributor.authorSulaiman, Muhammad
dc.contributor.authorBakar, Maharani A.
dc.date.accessioned2025-07-04T16:35:51Z
dc.date.available2025-07-04T16:35:51Z
dc.date.issued2022
dc.description.abstractIn this study, the intelligent computational strength of neural networks (NNs) based on the backpropagated Levenberg-Marquardt (BLM) algorithm is utilized to investigate the numerical solution of nonlinear multiorder fractional differential equations (FDEs). The reference data set for the design of the BLM-NN algorithm for different examples of FDEs are generated by using the exact solutions. To obtain the numerical solutions, multiple operations based on training, validation, and testing on the reference data set are carried out by the design scheme for various orders of FDEs. The approximate solutions by the BLM-NN algorithm are compared with analytical solutions and performance based on mean square error (MSE), error histogram (EH), regression, and curve fitting. This further validates the accuracy, robustness, and efficiency of the proposed algorithm.
dc.identifier.citationKhan, N. A., Ibrahim Khalaf, O., Andrés Tavera Romero, C., Sulaiman, M., & Bakar, M. A. (2022). Application of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/2710576
dc.identifier.issn16875265
dc.identifier.urihttps://repositorio.usc.edu.co/handle/20.500.12421/7174
dc.language.isoen
dc.publisherHindawi Limited
dc.titleApplication of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations
dc.typeArticle

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