Adaptation en ligne d ’ un dictionnaire pour les méthodes à noyau

This article tackles the online identification problem for nonlinear and nonstationary systems using kernel methods. The order of the model is controlled by the coherence criterion considered as a sparsification technique which leads to select the most relevant kernel functions to form a dictionary. We explore the dictionary adaptation using a stochastic gradient descent method along with an online kernel identification algorithm. For the latter, without limitation, it may be the kernel recursive least squares algorithm or the kernel affine projection algorithm. The proposed method leads to a reduction of the instantaneous quadratic error and to a decrease in the model’s order.

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