Support vector machines with composite kernels for nonlinear systems identification

In this paper, a nonlinear system identification based on support vector machines (SVM) has been addressed. A family of SVM-ARMA models is presented in order to integrate the input and the output in the reproducing kernel Hilbert space (RKHS). The performances of the different SVM-ARMA formulations...

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Bibliographic Details
Published in2008 International Multiconference on Computer Science and Information Technology pp. 113 - 118
Main Authors El Gonnouni, A., Lyhyaoui, A., El Jelali, S., Ramon, M.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Desktop publishing
Hilbert space
Kernel
Least squares methods
Neural networks
Nonlinear systems
Power system modeling
Support vector machine classification
Support vector machines
System identification
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Summary:In this paper, a nonlinear system identification based on support vector machines (SVM) has been addressed. A family of SVM-ARMA models is presented in order to integrate the input and the output in the reproducing kernel Hilbert space (RKHS). The performances of the different SVM-ARMA formulations for system identification are illustrated with two systems and compared with the least square method.
ISBN:8360810141
9788360810149
DOI:10.1109/IMCSIT.2008.4747226