• 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