title: Online prediction model based on the SVD-KPCA method.
abstract: 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.
authors.affiliation: Unité de Recherche d'Automatique, Traitement de Signal et Image (ATSI), Monastir 5000, Tunisia. ilyes.elaissi@yahoo.fr
authors.forename: Ilyes
authors.initials: I
authors.lastname: Elaissi
authors.forename: Ines
authors.initials: I
authors.lastname: Jaffel
authors.forename: Okba
authors.initials: O
authors.lastname: Taouali
authors.forename: Hassani
authors.initials: H
authors.lastname: Messaoud
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D009862:Online Systems; D025341:Principal Component Analysis