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Numerical approximations to the nonlinear fractional-order Logistic population model with fractional-order Bessel and Legendre bases

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  • Izadi, Mohammad
  • Srivastava, H.M.

Abstract

The main aim of this manuscript is to obtain the approximate solutions of the nonlinear Logistic equation of fractional order by developing a collocation approach based on the fractional-order Bessel and Legendre functions. The main characteristic of these polynomial approximation techniques is that they transform the governing differential equation into a system of algebraic equations, thus the computational efforts will be greatly reduced. Our secondary aim is to show a comparative investigation on the use of these fractional-order polynomials and to examine their utilities to solve the model problem. Numerical experiments are carried out to demonstrate the validity and applicability of the presented techniques and comparisons are made with methods available in the standard literature. The methods perform very well in terms of efficiency and simplicity to solve this population model especially when the Legendre bases are utilized.

Suggested Citation

  • Izadi, Mohammad & Srivastava, H.M., 2021. "Numerical approximations to the nonlinear fractional-order Logistic population model with fractional-order Bessel and Legendre bases," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:chsofr:v:145:y:2021:i:c:s0960077921001314
    DOI: 10.1016/j.chaos.2021.110779
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    References listed on IDEAS

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    1. Al-Mdallal, Qasem M. & Abu Omer, Ahmed S., 2018. "Fractional-order Legendre-collocation method for solving fractional initial value problems," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 74-84.
    2. Singh, Harendra & Srivastava, H.M., 2019. "Jacobi collocation method for the approximate solution of some fractional-order Riccati differential equations with variable coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1130-1149.
    3. Srivastava, H.M. & Dubey, V.P. & Kumar, R. & Singh, J. & Kumar, D. & Baleanu, D., 2020. "An efficient computational approach for a fractional-order biological population model with carrying capacity," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Singh, Jagdev, 2020. "Analysis of fractional blood alcohol model with composite fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Srivastava, H.M. & Saad, Khaled M. & Khader, M.M., 2020. "An efficient spectral collocation method for the dynamic simulation of the fractional epidemiological model of the Ebola virus," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Turalska, Malgorzata & West, Bruce J., 2017. "A search for a spectral technique to solve nonlinear fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 102(C), pages 387-395.
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    Cited by:

    1. Izadi, Mohammad & Roul, Pradip, 2022. "Spectral semi-discretization algorithm for a class of nonlinear parabolic PDEs with applications," Applied Mathematics and Computation, Elsevier, vol. 429(C).
    2. Izadi, Mohammad & Yüzbaşı, Şuayip & Adel, Waleed, 2022. "Accurate and efficient matrix techniques for solving the fractional Lotka–Volterra population model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    3. Izadi, Mohammad & Srivastava, H.M., 2021. "An efficient approximation technique applied to a non-linear Lane–Emden pantograph delay differential model," Applied Mathematics and Computation, Elsevier, vol. 401(C).
    4. Mohammad Izadi & Şuayip Yüzbaşi & Samad Noeiaghdam, 2021. "Approximating Solutions of Non-Linear Troesch’s Problem via an Efficient Quasi-Linearization Bessel Approach," Mathematics, MDPI, vol. 9(16), pages 1-16, August.
    5. Lan, Heng-you, 2021. "Approximation-solvability of population biology systems based on p-Laplacian elliptic inequalities with demicontinuous strongly pseudo-contractive operators," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).

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