Design Feed Forward Neural Network to Solve Singular Boundary Value ProblemsReport as inadecuate

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ISRN Applied MathematicsVolume 2013 2013, Article ID 650467, 7 pages

Research ArticleDepartment of Mathematics, College of Education Ibn Al-Haitham, Baghdad University, Iraq

Received 14 May 2013; Accepted 9 June 2013

Academic Editors: Z. Huang and X. Wen

Copyright © 2013 Luma N. M. Tawfiq and Ashraf A. T. Hussein. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The aim of this paper is to design feed forward neural network for solving second-order singular boundary value problems in ordinary differential equations. The neural networks use the principle of back propagation with different training algorithms such as quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. Two examples are considered to show that effectiveness of using the network techniques for solving this type of equations. The convergence properties of the technique and accuracy of the interpolation technique are considered.

Author: Luma N. M. Tawfiq and Ashraf A. T. Hussein



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