Modifying Explicit Finite Difference Method by Using Radial Basis Function Neural Network | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 14, Volume 10, Issue 2, June 2013, Pages 171-186 PDF (482.76 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2013.163484 | ||
| Author | ||
| Omar S. Kasim | ||
| College of Computer Science and Mathematics University of Mosul, Mosul, Iraq | ||
| Abstract | ||
| In this research, we use artificial neural networks, specifically radial basis function neural network (RBFNN) to improve the performance and work of the explicit finite differences method (EFDM), where it was compared, the modified method with an explicit finite differences method through solving the Murray equation and showing by comparing results with the exact solution that the improved method by using (RBFNN) is the best and most accurate by giving less error rate through root mean square error (RMSE) from the classical method (EFDM). | ||
| Keywords | ||
| artificial neural network; finite difference; Murray equation | ||
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