Statistical Learning for Systems Modeling - Taouali, Okba
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The work presented in this report falls within the framework of Machine Learning where we seek to model a non-linear system and to identify online the parameters of the model considered. This model is developed in a reproducing kernel Hilbert space (RKHS). These so-called representation or black box models are linear with respect to their parameters. They have had great success in identifying nonlinear systems using kernel methods.

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Produktbeschreibung
The work presented in this report falls within the framework of Machine Learning where we seek to model a non-linear system and to identify online the parameters of the model considered. This model is developed in a reproducing kernel Hilbert space (RKHS). These so-called representation or black box models are linear with respect to their parameters. They have had great success in identifying nonlinear systems using kernel methods.
Autorenporträt
Okba Taouali se doctoró en Ingeniería Eléctrica en 2010 por la École Nationale d'Ingénieurs de Monastir (ENIM). Actualmente es profesor en la ENIM (Túnez) y catedrático en el FCIT de la Universidad de Tabuk (Arabia Saudí). Sus intereses de investigación incluyen : Aprendizaje automático, métodos kernel, diagnóstico de fallos.