Skip to main content
Top
Published in: Evolutionary Intelligence 4/2021

15-09-2020 | Research Paper

Numerical solution of Bagley–Torvik equations using Legendre artificial neural network method

Authors: Akanksha Verma, Manoj Kumar

Published in: Evolutionary Intelligence | Issue 4/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this article, we have used the Legendre artificial neural network to find the solution of the Bagley–Torvik equation, which is a fractional-order ordinary differential equation. Caputo fractional derivative has been considered throughout the presented work to handle the fractional order differential equation. The training of optimal weights of the network has been carried out using a simulated annealing optimization technique. Here we have presented three examples to exhibit the precision and relevance of the proposed technique with comparison to the other numerical methods with error analysis. The proposed technique is an easy, highly efficient, and robust technique for finding the approximate solution of fractional-order ordinary differential equations.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Khan NA, Shaikh A, Sultan F, Ara A (2017) Numerical simulation using artificial neural network on fraction differential equations, World’s largest Science, Technology & Medicine Open Access Book Publisher, (August), pp 97–112 Khan NA, Shaikh A, Sultan F, Ara A (2017) Numerical simulation using artificial neural network on fraction differential equations, World’s largest Science, Technology & Medicine Open Access Book Publisher, (August), pp 97–112 
2.
go back to reference Bansal MK, Jain R (2016) Analytic solution of Bagle–Torvik equation by generalize differential transform. Int J Pure Appl Math 110(2):265–273CrossRef Bansal MK, Jain R (2016) Analytic solution of Bagle–Torvik equation by generalize differential transform. Int J Pure Appl Math 110(2):265–273CrossRef
4.
go back to reference Bagley RL, Torvik J (1983) Fractional calculus: a different approach to the analysis of viscoelastically damped structures. AIAA J 21(5):741–748CrossRef Bagley RL, Torvik J (1983) Fractional calculus: a different approach to the analysis of viscoelastically damped structures. AIAA J 21(5):741–748CrossRef
5.
go back to reference Torvik PJ, Bagley RL (1984) On the appearance of the fractional derivative in the behavior of real materials. J Appl Mech 51(2):294–298CrossRef Torvik PJ, Bagley RL (1984) On the appearance of the fractional derivative in the behavior of real materials. J Appl Mech 51(2):294–298CrossRef
6.
go back to reference Podlubny I (1998) Fractional differential equations. Academic Press, LondonMATH Podlubny I (1998) Fractional differential equations. Academic Press, LondonMATH
7.
go back to reference Diethelm K, Ford NJ (2002) Numerical solution of the Bagley–Torvik equation. Manch Centre Comput Math 42(3):490–507MathSciNetMATH Diethelm K, Ford NJ (2002) Numerical solution of the Bagley–Torvik equation. Manch Centre Comput Math 42(3):490–507MathSciNetMATH
8.
go back to reference Hu Y, Luo Y, Lu Z (2008) Analytical solution of the linear fractional differential equation by Adomian decomposition method. J Comput Appl Math 215:220–229MathSciNetCrossRef Hu Y, Luo Y, Lu Z (2008) Analytical solution of the linear fractional differential equation by Adomian decomposition method. J Comput Appl Math 215:220–229MathSciNetCrossRef
9.
go back to reference Castillo E, Cobo A, Gutierrez JM, Pruneda E (1999) Working with differential, functional and difference equations using functional networks. Appl Math Model 23:89–107CrossRef Castillo E, Cobo A, Gutierrez JM, Pruneda E (1999) Working with differential, functional and difference equations using functional networks. Appl Math Model 23:89–107CrossRef
10.
go back to reference Tomasiello S (2009) A functional network to predict fresh and hardened properties of self-compacting concretes. Int J Numer Methods Biomed Eng 27:840–847CrossRef Tomasiello S (2009) A functional network to predict fresh and hardened properties of self-compacting concretes. Int J Numer Methods Biomed Eng 27:840–847CrossRef
11.
go back to reference Erdem RT, Seker S, Ozturk AU, Gucuyen E (2013) Numerical analysis on corrosion resistance of mild steel structures. Eng Comput 29:529–533CrossRef Erdem RT, Seker S, Ozturk AU, Gucuyen E (2013) Numerical analysis on corrosion resistance of mild steel structures. Eng Comput 29:529–533CrossRef
12.
go back to reference Podlubny I, Skovranek T, Jara BMV (2009) Matrix approach to discretization of fractional derivatives and to solution of fractional differential equations and their systems. In: International conference on emerging technologies and factory automation (ETFA). IEEE, Mallorca, Spain, 22–25 Sept 2009 Podlubny I, Skovranek T, Jara BMV (2009) Matrix approach to discretization of fractional derivatives and to solution of fractional differential equations and their systems. In: International conference on emerging technologies and factory automation (ETFA). IEEE, Mallorca, Spain, 22–25 Sept 2009
13.
go back to reference El-Sayed AMA, El-Kalla IL, Ziada EAA (2010) Analytical and numerical solutions of multi-term nonlinear fractional orders differential equations. Appl Numer Math 60:788–797MathSciNetCrossRef El-Sayed AMA, El-Kalla IL, Ziada EAA (2010) Analytical and numerical solutions of multi-term nonlinear fractional orders differential equations. Appl Numer Math 60:788–797MathSciNetCrossRef
14.
go back to reference Kurnaz A, Cenesiz Y, Keskin Y (2010) The solution of the Bagley–Torvik equation with the generalized Taylor collocation method. J Frankl Inst 347(2):452–466MathSciNetCrossRef Kurnaz A, Cenesiz Y, Keskin Y (2010) The solution of the Bagley–Torvik equation with the generalized Taylor collocation method. J Frankl Inst 347(2):452–466MathSciNetCrossRef
16.
go back to reference Koker R (2013) A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators. Eng Comput 29:507–515CrossRef Koker R (2013) A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators. Eng Comput 29:507–515CrossRef
17.
go back to reference Loia V, Parente D, Pedrycz W, Tomasiello S (2018) A granular functional network with delay: some dynamical properties and application to the sign prediction in social networks. Neurocomputing 321:61–71CrossRef Loia V, Parente D, Pedrycz W, Tomasiello S (2018) A granular functional network with delay: some dynamical properties and application to the sign prediction in social networks. Neurocomputing 321:61–71CrossRef
18.
go back to reference Tomasiello S, Macias-Diaz JE, Khastan A, Alijani Z (2019) New sinusoidal basis functions and a neural network approach to solve nonlinear Volterra–Fredholm integral equations. Neural Comput Appl 31:4865–4878CrossRef Tomasiello S, Macias-Diaz JE, Khastan A, Alijani Z (2019) New sinusoidal basis functions and a neural network approach to solve nonlinear Volterra–Fredholm integral equations. Neural Comput Appl 31:4865–4878CrossRef
19.
go back to reference Verma A, Kumar M (2019) Numerical solution of Lane–Emden type equations using multilayer perceptron neural network method. Int J Appl Comput Math 141(5):1–14MathSciNetMATH Verma A, Kumar M (2019) Numerical solution of Lane–Emden type equations using multilayer perceptron neural network method. Int J Appl Comput Math 141(5):1–14MathSciNetMATH
21.
go back to reference Mekkaoui T, Hammouch Z (2012) Approximate analytical solutions to the Bagley–Torvik equation by the fractional iteration method. Ann Univ Craiova Math Comput Sci Ser 39(2):251–256MathSciNetMATH Mekkaoui T, Hammouch Z (2012) Approximate analytical solutions to the Bagley–Torvik equation by the fractional iteration method. Ann Univ Craiova Math Comput Sci Ser 39(2):251–256MathSciNetMATH
22.
go back to reference Mohammadi F (2014) Numerical solution of Bagley–Torvik equation using Chebyshev wavelet operational matrix of fractional derivative. Int J Adv Appl Math Mech 2(1):83–91MathSciNetMATH Mohammadi F (2014) Numerical solution of Bagley–Torvik equation using Chebyshev wavelet operational matrix of fractional derivative. Int J Adv Appl Math Mech 2(1):83–91MathSciNetMATH
23.
go back to reference Labecca W, Guimaraes O, Piqueira JRC (2015) Analytical solution of general Bagley–Torvik equation. Math Probl Eng 3:1–4MathSciNetCrossRef Labecca W, Guimaraes O, Piqueira JRC (2015) Analytical solution of general Bagley–Torvik equation. Math Probl Eng 3:1–4MathSciNetCrossRef
24.
go back to reference Popolizio M (2018) Numerical solution of multiterm fractional differential equations using the matrix Mittag–Leffler functions. Mathematics 6(1):1–13MathSciNetCrossRef Popolizio M (2018) Numerical solution of multiterm fractional differential equations using the matrix Mittag–Leffler functions. Mathematics 6(1):1–13MathSciNetCrossRef
25.
go back to reference Pang D, Jiang W, Du J, Ullah A, Niazi K (2019) Analytical solution of the generalized Bagley–Torvik equation. Adv Differ Equ 8:1–13MathSciNetMATH Pang D, Jiang W, Du J, Ullah A, Niazi K (2019) Analytical solution of the generalized Bagley–Torvik equation. Adv Differ Equ 8:1–13MathSciNetMATH
26.
go back to reference Caputo M (1967) Linear models of dissipation whose Q is almost. Geophys J R Astron Soc 13:529–539CrossRef Caputo M (1967) Linear models of dissipation whose Q is almost. Geophys J R Astron Soc 13:529–539CrossRef
27.
go back to reference Chakraverty S, Mall S (2014) Chebyshev neural network based model for solving Lane–Emden type equations. Appl Math Comput 247:100–114MathSciNetMATH Chakraverty S, Mall S (2014) Chebyshev neural network based model for solving Lane–Emden type equations. Appl Math Comput 247:100–114MathSciNetMATH
29.
go back to reference Raja MAZ, Samar R, Manzar MA, Shah SM (2017) Design of unsupervised fractional neural network model optimized with interior-point algorithm for solving Bagley–Torvik equation. Math Comput Simul 132:139–158MathSciNetCrossRef Raja MAZ, Samar R, Manzar MA, Shah SM (2017) Design of unsupervised fractional neural network model optimized with interior-point algorithm for solving Bagley–Torvik equation. Math Comput Simul 132:139–158MathSciNetCrossRef
30.
go back to reference Pedas A, Tamme E (2011) On the convergence of spline collocation methods for solving fractional differential equations. J Comput Appl Math 235:3502–3514MathSciNetCrossRef Pedas A, Tamme E (2011) On the convergence of spline collocation methods for solving fractional differential equations. J Comput Appl Math 235:3502–3514MathSciNetCrossRef
Metadata
Title
Numerical solution of Bagley–Torvik equations using Legendre artificial neural network method
Authors
Akanksha Verma
Manoj Kumar
Publication date
15-09-2020
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 4/2021
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-020-00481-x

Other articles of this Issue 4/2021

Evolutionary Intelligence 4/2021 Go to the issue

Premium Partner