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De l'identification des systèmes (hybrides et à sortie binaire) à l'extraction de motifs

Abstract : In this thesis, we deal with the identification of systems and the extraction of patterns from data. In the context of system identification, we focus precisely on the identification of hybrid systems and the identification of linear systems using binary sensors. Two very popular classes of hybrid systems are switched linear systems and piecewise affine systems. First, we give an overview of the different approaches available in the literature for the identification of these two classes. Then, we propose a new real-time identification algorithm for switched linear systems, it's based on an Outer Bounding Ellipsoid (OBE) type algorithm suitable for system identification with bounded noise. We then present several extensions of the algorithm either for the identification of piecewise affine systems, the identification of switched linear systems described by an output error model and the identification of MIMO switched linear systems. After this, we address the problem of the identification of linear systems using binary sensors by introducing an original point of view. We formulate the identification problem as a classification problem. This formulation allows the use of supervised learning algorithms such as Support Vector Machines (SVMs) for the identification of discrete time systems and the identification of continuous-time systems using binary sensors. In the context of pattern extraction, we first present an overview of the different pattern extraction algorithms and clustering techniques available in the literature. Next, we propose an algorithm for extracting patterns from data based on clustering techniques.
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Submitted on : Monday, March 26, 2018 - 5:08:41 PM
Last modification on : Friday, November 22, 2019 - 4:32:18 PM
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  • HAL Id : tel-01743839, version 1


Abdelhak Goudjil. De l'identification des systèmes (hybrides et à sortie binaire) à l'extraction de motifs. Automatique / Robotique. Normandie Université, 2017. Français. ⟨NNT : 2017NORMC240⟩. ⟨tel-01743839⟩



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