Identification de systèmes utilisant les réseaux de neurones : un compromis entre précision, complexité et charge de calculs.

Abstract : This report concerns the research topic of black box nonlinear system identification. In effect, among all the various and numerous techniques developed in this field of research these last decades, it seems still interesting to investigate the neural network approach in complex system model estimation. Even if accurate models have been derived, the main drawbacks of these techniques remain the large number of parameters required and, as a consequence, the important computational cost necessary to obtain the convenient level of the model accuracy desired. Hence, motivated to address these drawbacks, we achieved a complete and efficient system identification methodology providing balanced accuracy, complexity and cost models by proposing, firstly, new neural network structures particularly adapted to a very wide use in practical nonlinear system modeling, secondly, a simple and efficient model reduction technique, and, thirdly, a computational cost reduction procedure. It is important to notice that these last two reduction techniques can be applied to a very large range of neural network architectures under two simple specific assumptions which are not at all restricting. Finally, the last important contribution of this work is to have shown that this estimation phase can be achieved in a robust framework if the quality of identification data compels it. In order to validate the proposed system identification procedure, application examples driven in simulation and on a real process, satisfactorily validated all the contributions of this thesis, confirming all the interest of this work.
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Submitted on : Thursday, October 3, 2013 - 11:52:08 AM
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Héctor Manuel Romero Ugalde. Identification de systèmes utilisant les réseaux de neurones : un compromis entre précision, complexité et charge de calculs.. Autre. Ecole nationale supérieure d'arts et métiers - ENSAM, 2013. Français. ⟨NNT : 2013ENAM0001⟩. ⟨pastel-00869428⟩

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