Solving Fractional Order Differential Equations by Using Fractional Radial Basis Function Neural Network

Autor:	Rana Javadi, Hamid Mesgarani, Omid Nikan, Zakieh Avazzadeh
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	artificial neural networks RBF radial basis function fractional differential equations fractional RBF initial value problems Mathematics QA1-939
Zdroj:	Symmetry, Vol 15, Iss 6, p 1275 (2023)
Druh dokumentu:	article
ISSN:	2073-8994
DOI:	10.3390/sym15061275
Popis:	Fractional differential equations (FDEs) arising in engineering and other sciences describe nature sufficiently in terms of symmetry properties. This paper proposes a numerical technique to approximate ordinary fractional initial value problems by applying fractional radial basis function neural network. The fractional derivative used in the method is considered Riemann-Liouville type. This method is simple to implement and approximates the solution of any arbitrary point inside or outside the domain after training the ANN model. Finally, three examples are presented to show the validity and applicability of the method.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/942049841c6d4aac9e7dcf2624454085 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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