Literature DB >> 24807921

Approximate solutions to ordinary differential equations using least squares support vector machines.

Siamak Mehrkanoon, Tillmann Falck, Johan A K Suykens.   

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.

Year:  2012        PMID: 24807921     DOI: 10.1109/TNNLS.2012.2202126

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw Learn Syst        ISSN: 2162-237X            Impact factor:   10.451


  1 in total

1.  Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines Using the Theory of Functional Connections.

Authors:  Carl Leake; Hunter Johnston; Lidia Smith; Daniele Mortari
Journal:  Mach Learn Knowl Extr       Date:  2019-10-09
  1 in total

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