Support vector machines with composite kernels for nonlinear systems identification

Amina El Gonnouni,Abdelouahid Lyhyaoui,Soufiane El Jelali,Manel Martínez-Ramón,El Gonnouni, A.,El Jelali, S.

Wisia（2008）

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摘要

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.

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关键词

autoregressive moving average processes,identification,least squares approximations,nonlinear systems,support vector machines,SVM-ARMA models,composite kernels,least square method,nonlinear systems identification,reproducing kernel Hilbert space,support vector machines

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