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Vol.3 , No. 1, Publication Date: Mar. 28, 2016, Page: 1-6
 is developed for obtaining the solution of the ordinary differential equations in an analytical function form. The approximate solution procedure is based upon forming of support vector machines (SVM’s) whose parameters are adjusted to solve a quadratic programming problem. The details of the method are discussed, and the capabilities of the method are illustrated by solving some differential equations. The performance of the method and the accuracy of the results are evaluated by comparing with the available numerical and analytical solutions.&picture=http://www.aascit.org/aascit/decorators/img/journal/912.jpg)
| [1] | Mojtaba Baymani, Department of Computer and Mathematics, Quchan University of Advanced Technology, Quchan, Iran. |
| [2] | Omid Teymoori, Department of Computer and Mathematics, Quchan University of Advanced Technology, Quchan, Iran. |
| [3] | Seyed GHasem Razavi, Department of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran. |
In this paper, a new method based on ϵ-Least Square Support Vector Machines (ϵ-LSSVM’s) is developed for obtaining the solution of the ordinary differential equations in an analytical function form. The approximate solution procedure is based upon forming of support vector machines (SVM’s) whose parameters are adjusted to solve a quadratic programming problem. The details of the method are discussed, and the capabilities of the method are illustrated by solving some differential equations. The performance of the method and the accuracy of the results are evaluated by comparing with the available numerical and analytical solutions.
Keywords
Ordinary Differential Equation, ϵ-Least Squares Support Vector Machine, Quadratic Programming Problem, Constrained Optimization Problem
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