Skip to main content

Advertisement

Log in

Design of stochastic numerical solver for the solution of singular three-point second-order boundary value problems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), i.e., ANN–GA–IPA. The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems. Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach. The comparison of the proposed results of ANN–GA–IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Gupta CP (1992) Solvability of a three-point nonlinear boundary value problem for a second order ordinary differential equation. J Math Anal Appl 168(2):540–551

    Article  MathSciNet  Google Scholar 

  2. Geng F (2009) Solving singular second order three-point boundary value problems using reproducing kernel Hilbert space method. Appl Math Comput 215(6):2095–2102

    MathSciNet  MATH  Google Scholar 

  3. Zhang Q, Jiang D (2008) Upper and lower solutions method and a second order three-point singular boundary value problem. Comput Math Appl 56(4):1059–1070

    Article  MathSciNet  Google Scholar 

  4. Agarwal RP et al (2003) Two-point higher-order BVPs with singularities in phase variables. Comput Math Appl 46(12):1799–1826

    Article  MathSciNet  Google Scholar 

  5. Arqub OA, Rashaideh H (2018) The RKHS method for numerical treatment for integrodifferential algebraic systems of temporal two-point BVPs. Neural Comput Appl 30(8):2595–2606

    Article  Google Scholar 

  6. Momani S, Abo-Hammour ZS, Alsmadi OM (2016) Solution of inverse kinematics problem using genetic algorithms. Appl Math Inf Sci 10(1):225

    Article  Google Scholar 

  7. Raja MAZ et al (2018) Design of artificial neural network models optimized with sequential quadratic programming to study the dynamics of nonlinear Troesch’s problem arising in plasma physics. Neural Comput Appl 29(6):83–109

    Article  Google Scholar 

  8. Schaff JC et al (2016) Numerical approach to spatial deterministic-stochastic models arising in cell biology. PLoS Comput Biol 12(12):e1005236

    Article  Google Scholar 

  9. Pelletier F, Masson C, Tahan A (2016) Wind turbine power curve modelling using artificial neural network. Renew Energy 89:207–214

    Article  Google Scholar 

  10. Umar M et al (2019) Intelligent computing for numerical treatment of nonlinear prey–predator models. Appl Soft Comput 80:506–524

    Article  Google Scholar 

  11. Raja MAZ, Junaid AK, Tahira H (2015) Stochastic numerical treatment for thin film flow of third grade fluid using unsupervised neural networks. J Taiwan Inst Chem Eng 48:26–39

    Article  Google Scholar 

  12. Soize C (2012) Stochastic models of uncertainties in computational structural dynamics and structural acoustics. nondeterministic mechanics. Springer, Vienna, pp 61–113

    MATH  Google Scholar 

  13. Effati S, Pakdaman M (2010) Artificial neural network approach for solving fuzzy differential equations. Inf Sci 180(8):1434–1457

    Article  MathSciNet  Google Scholar 

  14. Sabir Z et al (2018) Neuro-heuristics for nonlinear singular Thomas–Fermi systems. Appl Soft Comput 65:152–169

    Article  Google Scholar 

  15. Raja MAZ et al (2018) A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. Eur Phys J Plus 133(9):364

    Article  Google Scholar 

  16. Raja MAZ et al (2016) Stochastic numerical solver for nanofluidic problems containing multi-walled carbon nanotubes. Appl Soft Comput 38:561–586

    Article  Google Scholar 

  17. Ahmad I, Ahmad S, Awais M et al (2018) Neuro-evolutionary computing paradigm for Painlevé equation-II in nonlinear optics. Eur Phys J Plus 133(5):184

    Article  Google Scholar 

  18. Zhang Z et al (2013) Stochastic testing method for transistor-level uncertainty quantification based on generalized polynomial chaos. IEEE Trans Comput Aided Des Integr Circuits Syst 32(10):1533–1545

    Article  Google Scholar 

  19. He W, Chen Y, Yin Z (2015) Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern 46(3):620–629

    Article  Google Scholar 

  20. Raja MAZ et al (2019) Numerical solution of doubly singular nonlinear systems using neural networks-based integrated intelligent computing. Neural Comput Appl 31(3):793–812

    Article  Google Scholar 

  21. Raja MAZ, Aslam MS, Chaudhary NI, Nawaz M, Shah SM (2019) Design of hybrid nature-inspired heuristics with application to active noise control systems. Neural Comput Appl 31(7):2563–2591

    Article  Google Scholar 

  22. Raja MAZ, Aslam MS, Chaudhary NI, Khan WU (2018) Bio-inspired heuristics hybrid with interior-point method for active noise control systems without identification of secondary path. Front Inf Technol Electr Eng 19(2):246–259

    Article  Google Scholar 

  23. Mehmood A et al (2019) Integrated intelligent computing paradigm for the dynamics of micropolar fluid flow with heat transfer in a permeable walled channel. Appl Soft Comput 79:139–162

    Article  Google Scholar 

  24. Raja MAZ, Mehmood A, Khan AA et al (2019) Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04157-1

    Article  Google Scholar 

  25. Ahmad I et al (2019) Design of computational intelligent procedure for thermal analysis of porous fin model. Chin J Phys 59:641–655

    Article  MathSciNet  Google Scholar 

  26. Zameer A et al (2019) Bio-inspired heuristics for layer thickness optimization in multilayer piezoelectric transducer for broadband structures. Soft Comput 23(10):3449–3463

    Article  Google Scholar 

  27. Ahmad I, Ilyas H, Urooj A et al (2019) Novel applications of intelligent computing paradigms for the analysis of nonlinear reactive transport model of the fluid in soft tissues and microvessels. Neural Comput Appl 31:1–19

    Google Scholar 

  28. Raja MAZ, Mehmood A, Niazi SA, Shah SM (2018) Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system. Neural Comput Appl 30(6):1905–1924

    Article  Google Scholar 

  29. Mehmood A et al (2018) Intelligent computing to analyze the dynamics of magnetohydrodynamic flow over stretchable rotating disk model. Appl Soft Comput 67:8–28

    Article  Google Scholar 

  30. Raja MAZ, Shah AA, Mehmood A, Chaudhary NI, Aslam MS (2018) Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system. Neural Comput Appl 29(12):1455–1474

    Article  Google Scholar 

  31. Ahmad I et al (2018) Neuro-evolutionary computing paradigm for Painlevé equation-II in nonlinear optics. Eur Phys J Plus 133(5):184

    Article  Google Scholar 

  32. Ahmad I et al (2018) Intelligent computing to solve fifth-order boundary value problem arising in induction motor models. Neural Comput Appl 29(7):449–466

    Article  Google Scholar 

  33. Raja MAZ, Shah FH, Syam MI (2018) Intelligent computing approach to solve the nonlinear Van der Pol system for heartbeat model. Neural Comput Appl 30(12):3651–3675

    Article  Google Scholar 

  34. de Klerk E, Frank V (2016) On the Turing model complexity of interior point methods for semidefinite programming. SIAM J Optim 26(3):1944–1961

    Article  MathSciNet  Google Scholar 

  35. Achache M, Tabchouche N (2019) A full-Newton step feasible interior-point algorithm for monotone horizontal linear complementarity problems. Optim Lett 13(5):1039–1057

    Article  MathSciNet  Google Scholar 

  36. Stefanova M et al (2018) An interior-point method-based solver for simulation of aircraft parts riveting. Eng Optim 50(5):781–796

    Article  MathSciNet  Google Scholar 

  37. Umenberger J, Manchester IR (2018) Specialized Interior-Point Algorithm for Stable Nonlinear System Identification. IEEE Trans Autom Control 64(6):2442–2456

    Article  MathSciNet  Google Scholar 

  38. Breedveld S, van den Berg B, Heijmen B (2017) An interior-point implementation developed and tuned for radiation therapy treatment planning. Comput Optim Appl 68(2):209–242

    Article  MathSciNet  Google Scholar 

  39. Weldeyesus AG, Jacek G (2018) "A specialized primal-dual interior point method for the plastic truss layout optimization". Comput Optim Appl 71(3):613–640

    Article  MathSciNet  Google Scholar 

  40. Muhammad Y, Khan R, Ullah F et al (2019) Design of fractional swarming strategy for solution of optimal reactive power dispatch. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04589-9

    Article  Google Scholar 

  41. Akbar S, Zaman F, Asif M, Rehman AU et al (2019) Novel application of FO-DPSO for 2-D parameter estimation of electromagnetic plane waves. Neural Comput Appl 31(8):3681–3690

    Article  Google Scholar 

  42. Zameer A, Muneeb M, Mirza SM et al (2020) Fractional-order particle swarm based multi-objective PWR core loading pattern optimization. Ann Nucl Energy 135:106982

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dumitru Baleanu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sabir, Z., Baleanu, D., Shoaib, M. et al. Design of stochastic numerical solver for the solution of singular three-point second-order boundary value problems. Neural Comput & Applic 33, 2427–2443 (2021). https://doi.org/10.1007/s00521-020-05143-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05143-8

Keywords

Navigation