- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
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, 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 | ||
|
Statistics Article View: 312 PDF Download: 308 |
||