Online identification of nonlinear system using reduced kernel principal component analysis |
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Authors: | Okba Taouali Ilyes Elaissi Hassani Messaoud |
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Affiliation: | (1) Unit? de Recherche d’Automatique, Traitement de Signal et Image (ATSI), 5000 Monastir, Tunisia |
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Abstract: | 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. |
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Keywords: | |
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