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
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);
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2012.2202126