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Reseach Article

Numerical Solution of Sixth-Order Differential Equations Arising in Astrophysics by Neural Network

by M. Khalid, Mariam Sultana, Faheem Zaidi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 6
Year of Publication: 2014
Authors: M. Khalid, Mariam Sultana, Faheem Zaidi
10.5120/18752-0023

M. Khalid, Mariam Sultana, Faheem Zaidi . Numerical Solution of Sixth-Order Differential Equations Arising in Astrophysics by Neural Network. International Journal of Computer Applications. 107, 6 ( December 2014), 1-6. DOI=10.5120/18752-0023

@article{ 10.5120/18752-0023,
author = { M. Khalid, Mariam Sultana, Faheem Zaidi },
title = { Numerical Solution of Sixth-Order Differential Equations Arising in Astrophysics by Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 6 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number6/18752-0023/ },
doi = { 10.5120/18752-0023 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:19.912169+05:30
%A M. Khalid
%A Mariam Sultana
%A Faheem Zaidi
%T Numerical Solution of Sixth-Order Differential Equations Arising in Astrophysics by Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 6
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the current paper, a neural network method to solve sixth-order differential equations and their boundary conditions has been presented. The idea this method incorporates is to integrate knowledge about the differential equation and its boundary conditions into neural networks and the training sets. Neural networks are being used incessantly to solve all kinds of problems hailing a wide range of disciplines. Several examples are given to illustrate the efficiency and implementation of the Neural Network method.

References
  1. Schalkoff, R. J. (1997) Artificial Neural Networks. McGraw-Hill, New York.
  2. Picton, P. (2000) Neural Networks. 2nd ed. Palgrave, Great Britain.
  3. Rosenblatt, F. (1957) The perceptron - a perceiving and recognizing automation. Comell Aeronautical Laboratory, Report 85–460.
  4. Boutayeb, A. & Twizell, E. (1992) Numerical methods for the solution of special sixth-order boundary value problems. International Journal of Computer Mathematics,45. pp 207-233
  5. Twizell, E. & Boutayeb, E. (1990) Numerical methods for the solution of special and general sixth-order boundary value problems, with applications to Benard layer eigenvalue problems. Proceedings of the Royal Socity London Series A. 431(1883). pp 433-450
  6. Baldwin, P. (1987) Asymptotic estimates of the eigenvalues of a sixth-order boundary-value problem obtained by using global phase-integral methods. Philosophical Transactions of the Royal Society of London Series A, 322(1566). pp 281-305
  7. Siddiqi, S. S. & Twizell, E. H. (1996) Spline solutions of linear sixth-order boundary-value problems. International Journal of Computer Mathematics, 60(3-4). pp 295-304
  8. El-Gamel, M. , Cannon, J. R. & Zayed, A. I. (2004) Sinc- Galerkin method for solving linear sixth-order boundaryvalue problems. Mathematics of Computation, 73(247). pp 1325-1343
  9. Agarwal, R. P. (1986) Boundary Value Problems for Higher Order Differential Equations. World Scientific Publishing, Singapore
  10. Shen, J. (1994) Efficient spectral-Galerkin method. I. Direct solvers of second- and fourth-order equations using Legendre polynomials. SIAM Journal on Scientific Computing, 15(6). pp 1489-1505
  11. Doha, E. H. & Bhrawy, A. H. (2006) Efficient spectral- Galerkin algorithms for direct solution for second-order differential equations using Jacobi polynomials. Numerical Algorithms, 42(2). pp 137-164
  12. Doha, E. H. & Bhrawy, A. H. (2008) Efficient spectral- Galerkin algorithms for direct solution of fourth-order differential equations using Jacobi polynomials. Applied Numerical Mathematics, 58(8). pp 1224-1244
  13. Doha, E. H. & Bhrawy, A. H. (2009) A Jacobi spectral Galerkin method for the integrated forms of fourth-order elliptic differential equations. Numerical Methods for Partial Differential Equations, 25(3). pp 712-739
  14. Doha, E. H. , Bhrawy, A. H. & Abd-Elhameed, W. M. (2009) Jacobi spectral Galerkin method for elliptic Neumann problems. Numerical Algorithms, 50(1). pp 67-91
  15. Doha, E. H. , Bhrawy, A. H. & Hafez, R. M. (2011) A Jacobi- Jacobi dual-Petrov-Galerkin method for third- and fifthorder differential equations. Mathematical and Computer Modelling, 53(9-10). pp 1820-1832
  16. Doha, E. H. , Bhrawy, A. H. & Hafez, R. M. (2011) A Jacobi dual-Petrov-Galerkin method for solving some odd-order ordinary differential equations. Abstract and Applied Analysis, Artical ID 947230, 21 pages
  17. Bhrawy, A. H. & Abd-Elhameed, W. M. (2011) New algorithm for the numerical solutions of nonlinear thirdorder differential equations using Jacobi-Gauss collocation method. Mathematical Problems in Engineering, Article ID 837218, 14 pages
  18. Doha, E. H. , Bhrawy, A. H. & Saker, M. A. (2011) Integrals of Bernstein polynomials: an application for the solution of high even-order differential equations. Applied Mathematics Letters, 24(4). pp 559-565
  19. Doha, E. H. , Bhrawy, A. H. & Saker, M. A. (2011) On the derivatives of Bernstein polynomials: an application for the solution of high even-order differential equations. Boundary Value Problem, Artical ID 829543, 16 pages
  20. Loghmani, G. B. & Ahmadinia, M. (2007) Numerical solution of sixth order boundary value problems with sixth degree B-spline functions. Applied Mathematics and Computation, 186(2—). pp 992-999
  21. Siddiqi, S. S. & Akram, G. (2008) Septic spline solutions of sixth-order boundary value problems. Journal of Computational and Applied Mathematics, 215(1). pp 288-301
  22. Wazwaz, A. (2001) The numerical solution of sixth-order boundary value problems by the modified decomposition method. Applied Mathematics and Computation, 118(2-3). pp 311-325
  23. Bhrawy, A. H. (2009) Legendre-Galerkin method for sixthorder boundary value problems. Journal of the Egyptian Mathematical Society, 17(2). pp 173-188
  24. Lee. H. & Kang, I. S. (1990) Neural algorithm for solving differential equations. Journal of Computational Physics, 91(1). pp 110-131
  25. Meade, A. J. & Femandez, A. A. (1994) The numerical solution of linear ordinary differential equations by feedforward neural networks. Mathematical and Computer Modelling, 19(12). pp 1-25
  26. Meade, A. J. & Femandez, A. A. (1994) Solution of nonlinear ordinary differential equations by feedforward neural networks. Mathematical and Computer Modelling, 20(9). pp 19-44
  27. Lagaris, I. E. , Likas, A. & Fotiadis, D. I. (1998) Artificial neural networks for solving ordinary and partial differential equations. IEEE Transactions on Neural Networks, 9(5) pp 987-1000
  28. Lagaris, I. E. , Likas, A. & Papageorgiou, D. G. (2000) Neural-network methods for boundary value problems with irregular boundaries. IEEE Transactions on Neural Networks, 11(5). pp 1041-1049
  29. Parisi, D. R. , Mariani, M. C. & Laborde, M. A. (2003) Solving differential equations with unsupervised neural networks. Chemical Engineering and Processing, 42(8-9). pp 715-721
  30. Malek, A. & Shekari, B. (2006) Numerical solution for high order differential equations using a hybrid neural network-Optimization method. Applied Mathematics and Computation, 183(1). pp 260-271
  31. Choi, B. & Lee, J. H. (2009) Comparison of generalization ability on solving differential equations using backpropagation and reformulated radial basis function networks. Neurocomputing, 73(1-3). pp 115-118
  32. Yazdi, H. S. , Pakdaman, M. & Modaghegh, H. (2011) Unsupervised kernel least mean square algorithm for solving ordinary differential equations. Neurocomputing, 74(12- 13). pp 2062-2071
  33. Selvaraju, N. & Samant, A. (2010) Solution of matrix Riccati differential equation for nonlinear singular system using neural networks. International Journal of Computer Applications, 29. pp 48-54
  34. He, S. , Reif, K. & Unbehauen, R. (2000) Multilayer neural networks for solving a class of partial differential equations. Neural Networks, 13(3). pp 385-396
  35. Kumar, M. & Yadav, N. (2011) Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: a survey. Computers and Mathematics with Applications, 62(10). pp 3796-3811
  36. Tsoulos, I. G. , Gavrilis, D. & Glavas, E. (2009) Solving differential equations with constructed neural networks. Neurocomputing, 72(10-12). pp 2385-2391
  37. Jianyu, L. , Siwei, L, Yingjian, Q. & Yaping, H. (2003) Numerical solution of elliptic partial differential equation using radial basis function neural networks. Neural Networks, 16(5-6). pp 729-734
  38. Shirvany, Y. , Hayati, M. & Moradian, R. (2009) Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations. Applied Soft Computing Journal, 9(1). ppp 20- 29
  39. Mai-Duy, N. & Tran-Cong, T. (2001) Numerical solution of differential equations using multiquadric radial basis function networks. Neural Networks, 14(2). pp 185-199
  40. Leephakpreeda, T. (2002) Novel determination of differential-equation solutions: universal approximation method. Journal of Computational and Applied Mathematics, 146(2). pp 443-457
  41. Franke, C. & Schaback, R. (1998) Solving partial differential equations by collocation using radial basis functions. Applied Mathematics and Computation, 93(1). pp 73-82
  42. Smaoui, N. & Al-Enezi, S. (2004) Modelling the dynamics of nonlinear partial differential equations using neural networks. Journal of Computational and Applied Mathematics, 170(1). pp 27-58
  43. Tsoulos, I. G. & Lagaris, I. E. (2006) Solving differential equations with genetic programming. Genetic Programming and Evolvable Machines, 7(1). pp 33-54
  44. McFall, K. S. & Mahan, J. R. (2009) Artificial neural network method for solution of boundary value problems with exact satisfaction of arbitrary boundary conditions. IEEE Transactions on Neural Networks, 20(8). pp 1221-1233
  45. Hoda, S. A. & Nagla, H. A. (2011) Neural network methods for mixed boundary value problems. International Journal of Nonlinear Science, 11. pp 312-316
  46. Zurada, J. M. (1994) Introduction to Artificial Neural Network. West Publishing.
  47. Haykin, S. (1999) Neural Networks a Comprehensive Foundation. Prentice Hall, New York.
Index Terms

Computer Science
Information Sciences

Keywords

Sixth Order Differential Equation Boundary Conditions Neural Network