This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). The proposed SVD-KPCA method uses the Singular Value Decomposition (SVD) technique to update the principal components. Then we use the Reduced Kernel Principal Component Analysis (RKPCA) to approach the principal components which represent the observations selected by the KPCA method.
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| http://dx.doi.org/10.1016/j.isatra.2012.09.007 | DOI Listing |
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Online prediction model based on the SVD-KPCA method.
ISA Trans
January 2013
Unité de Recherche d'Automatique, Traitement de Signal et Image (ATSI), Monastir 5000, Tunisia.
This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). The proposed SVD-KPCA method uses the Singular Value Decomposition (SVD) technique to update the principal components. Then we use the Reduced Kernel Principal Component Analysis (RKPCA) to approach the principal components which represent the observations selected by the KPCA method.
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