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Taouali O.,Unite de Recherche dAutomatique | Elaissi I.,Unite de Recherche dAutomatique | Messaoud H.,Unite de Recherche dAutomatique
Neural Computing and Applications | Year: 2012

The Principal Component Analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets. It is readily performed by solving an eigenvalue problem, or by using iterative algorithms that estimate principal components. This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). Therefore, the PCA technique is tuned twice, first we exploit the Kernel PCA (KPCA) which is a nonlinear extension of the PCA to RKHS as it transforms the input data by a nonlinear mapping into a high-dimensional feature space to which the PCA is performed. Second, we use the Reduced Kernel Principal Component Analysis (RKPCA) to update the principal components that represent the observations selected by the KPCA method. © 2010 Springer-Verlag London Limited.


Elaissi I.,Unite de Recherche dAutomatique | Jaffel I.,Unite de Recherche dAutomatique | Taouali O.,Unite de Recherche dAutomatique | Messaoud H.,Unite de Recherche dAutomatique
ISA Transactions | Year: 2013

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. © 2012 ISA.


PubMed | Unite de Recherche dAutomatique
Type: Journal Article | Journal: ISA transactions | Year: 2013

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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