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

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  • S M, Sivalingam
  • Kumar, Pushpendra
  • Govindaraj, V.

Abstract

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.

Suggested Citation

  • S M, Sivalingam & Kumar, Pushpendra & Govindaraj, V., 2023. "A novel numerical scheme for fractional differential equations using extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    • Handle: RePEc:eee:phsmap:v:622:y:2023:i:c:s0378437123004429
    DOI: 10.1016/j.physa.2023.128887
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    References listed on IDEAS

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    1. Odibat, Zaid & Momani, Shaher, 2008. "Modified homotopy perturbation method: Application to quadratic Riccati differential equation of fractional order," Chaos, Solitons & Fractals, Elsevier, vol. 36(1), pages 167-174.
    2. Quan, Ho Dac & Huynh, Hieu Trung, 2023. "Solving partial differential equation based on extreme learning machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 205(C), pages 697-708.
    3. Pourbabaee, Marzieh & Saadatmandi, Abbas, 2019. "A novel Legendre operational matrix for distributed order fractional differential equations," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 215-231.
    4. Talib, Imran & Noor, Zulfiqar Ahmad & Hammouch, Zakia & Khalil, Hammad, 2022. "Compatibility of the Paraskevopoulos’s algorithm with operational matrices of Vieta–Lucas polynomials and applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 442-463.
    5. Shloof, A.M. & Senu, N. & Ahmadian, A. & Salahshour, Soheil, 2021. "An efficient operation matrix method for solving fractal–fractional differential equations with generalized Caputo-type fractional–fractal derivative," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 415-435.
    6. X. Y. Li & B. Y. Wu & R. T. Wang, 2014. "Reproducing Kernel Method for Fractional Riccati Differential Equations," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-6, April.
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    Cited by:

    1. Sivalingam, S M & Kumar, Pushpendra & Trinh, Hieu & Govindaraj, V., 2024. "A novel L1-Predictor-Corrector method for the numerical solution of the generalized-Caputo type fractional differential equations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 462-480.

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