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Stochastic numerical approach for solving second order nonlinear singular functional differential equation

Author

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  • Sabir, Zulqurnain
  • Wahab, Hafiz Abdul
  • Umar, Muhammad
  • Erdoğan, Fevzi

Abstract

A new computational intelligence numerical scheme is presented for the solution of second order nonlinear singular functional differential equations (FDEs) using artificial neural networks (ANNs), global operator genetic algorithms (GAs), efficient local operator interior-point algorithm (IPA), and the hybrid combination of GA-IPA. An unsupervised error function is assembled for the DDE optimized by ANNs using the hybrid combination of GA-IPA. Three kinds of the second order nonlinear singular DDEs have been solved numerically and compared their results with the exact solutions to authenticate the performance and exactness of the present designed scheme. Moreover, statistical analysis based on Mean absolute deviation, Theil's inequality coefficient and Nash Sutcliffe efficiency is also performed to validate the convergence and accuracy of the present scheme.

Suggested Citation

  • Sabir, Zulqurnain & Wahab, Hafiz Abdul & Umar, Muhammad & Erdoğan, Fevzi, 2019. "Stochastic numerical approach for solving second order nonlinear singular functional differential equation," Applied Mathematics and Computation, Elsevier, vol. 363(C), pages 1-1.
  • Handle: RePEc:eee:apmaco:v:363:y:2019:i:c:21
    DOI: 10.1016/j.amc.2019.124605
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    1. Omar Abu Arqub & Ahmad El-Ajou & A. Sami Bataineh & I. Hashim, 2013. "A Representation of the Exact Solution of Generalized Lane-Emden Equations Using a New Analytical Method," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-10, July.
    2. Ximei Yang & Hongwei Liu & Yinkui Zhang, 2015. "A New Strategy in the Complexity Analysis of an Infeasible-Interior-Point Method for Symmetric Cone Programming," Journal of Optimization Theory and Applications, Springer, vol. 166(2), pages 572-587, August.
    3. M.K. Kadalbajoo & K.K. Sharma, 2002. "Numerical Analysis of Boundary-Value Problems for Singularly-Perturbed Differential-Difference Equations with Small Shifts of Mixed Type," Journal of Optimization Theory and Applications, Springer, vol. 115(1), pages 145-163, October.
    4. Nishimura, Etsuko & Imai, Akio & Papadimitriou, Stratos, 2001. "Berth allocation planning in the public berth system by genetic algorithms," European Journal of Operational Research, Elsevier, vol. 131(2), pages 282-292, June.
    5. Pelletier, Francis & Masson, Christian & Tahan, Antoine, 2016. "Wind turbine power curve modelling using artificial neural network," Renewable Energy, Elsevier, vol. 89(C), pages 207-214.
    6. M. Pirhaji & M. Zangiabadi & H. Mansouri, 2017. "An $$\ell _{2}$$ ℓ 2 -neighborhood infeasible interior-point algorithm for linear complementarity problems," 4OR, Springer, vol. 15(2), pages 111-131, June.
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    Cited by:

    1. Sabir, Zulqurnain & Raja, Muhammad Asif Zahoor & Wahab, Hafiz Abdul & Altamirano, Gilder Cieza & Zhang, Yu-Dong & Le, Dac-Nhuong, 2021. "Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 87-101.
    2. Zulqurnain Sabir & Adnène Arbi & Atef F. Hashem & Mohamed A Abdelkawy, 2023. "Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model," Mathematics, MDPI, vol. 11(21), pages 1-20, October.
    3. Ali, Karmina K. & Yokus, Asıf & Seadawy, Aly R. & Yilmazer, Resat, 2022. "The ion sound and Langmuir waves dynamical system via computational modified generalized exponential rational function," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    4. Sabir, Zulqurnain & Saoud, Sahar & Raja, Muhammad Asif Zahoor & Wahab, Hafiz Abdul & Arbi, Adnène, 2020. "Heuristic computing technique for numerical solutions of nonlinear fourth order Emden–Fowler equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 534-548.
    5. Jadoon, Ihtesham & Raja, Muhammad Asif Zahoor & Junaid, Muhammad & Ahmed, Ashfaq & Rehman, Ata ur & Shoaib, Muhammad, 2021. "Design of evolutionary optimized finite difference based numerical computing for dust density model of nonlinear Van-der Pol Mathieu’s oscillatory systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 444-470.

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