A novel numerical scheme for fractional differential equations using extreme learning machine

In this paper, we propose a neural network-based approach with an Extreme Learning Machine (ELM) for solving fractional differential equations. The solution procedure for the linear and nonlinear fractional differential equations has been derived. Also the convergence and stability of the proposed m...

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Veröffentlicht in:Physica A 2023-07, Vol.622, p.128887, Article 128887
Hauptverfasser: S M, Sivalingam, Kumar, Pushpendra, Govindaraj, V.
Format: Artikel
Sprache:eng
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Zusammenfassung:In this paper, we propose a neural network-based approach with an Extreme Learning Machine (ELM) for solving fractional differential equations. The solution procedure for the linear and nonlinear fractional differential equations has been derived. Also the convergence and stability of the proposed method is provided. Then we examine the numerical solution of several fractional-order ordinary and partial differential equations. As a last example the Burgers equation without an explicit exact solution. The effect of changing the number of neurons on the accuracy of the solution is obtained graphically. •A novel scheme for solving fractional differential equations is proposed.•The neural network approach with Extreme Learning Machine is used.•The convergence and stability of the proposed method are provided in detail.•The method is validated by several problems.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2023.128887