DocumentCode
1547131
Title
Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines
Author
Mehrkanoon, S. ; Falck, T. ; Suykens, J.A.K.
Author_Institution
Dept. of Electr. Eng., Katholieke Univ. Leuven, Leuven, Belgium
Volume
23
Issue
9
fYear
2012
Firstpage
1356
Lastpage
1367
Abstract
In this paper, a new approach based on least squares support vector machines (LS-SVMs) is proposed for solving linear and nonlinear ordinary differential equations (ODEs). The approximate solution is presented in closed form by means of LS-SVMs, whose parameters are adjusted to minimize an appropriate error function. For the linear and nonlinear cases, these parameters are obtained by solving a system of linear and nonlinear equations, respectively. The method is well suited to solving mildly stiff, nonstiff, and singular ODEs with initial and boundary conditions. Numerical results demonstrate the efficiency of the proposed method over existing methods.
Keywords
least squares approximations; linear differential equations; nonlinear differential equations; support vector machines; LS-SVM; ODE; approximate solutions; least squares support vector machines; linear ordinary differential equations; nonlinear ordinary differential equations; Differential equations; Kernel; Least squares approximation; Manganese; Mathematical model; Optimization; Closed-form approximate solution; collocation method; least squares support vector machines (LS-SVMs); ordinary differential equations (ODEs);
fLanguage
English
Journal_Title
Neural Networks and Learning Systems, IEEE Transactions on
Publisher
ieee
ISSN
2162-237X
Type
jour
DOI
10.1109/TNNLS.2012.2202126
Filename
6224185
Link To Document