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Adaptations et applications de modèles mixtes de réseaux de neurones à un processus industriel

Abstract : This study is interested in analyzing the contribution of artificial neural networks in order to improve the control of complex industrial processes that are mainly characterized by their temporal behavior. The main motivations of the time series analysis are data reduction, indexation based on similarity, localization of sequences, knowledge extraction and prediction. The analyzed industrial process is an electric arc furnace for the liquid steel production in Luxembourg. The proposed approach is a concept of predictive control based on unsupervised learning techniques with the aim of knowledge extraction. Our signal coding method is based on primitive patterns that compose the signals. These patterns, building the coding alphabet, are extracted using an unsupervised method, the self organizing maps of Kohonen (SOM). An alphabet validation approach is proposed. One of the important subjects of this research is the similarity of time series. The proposed method is unsupervised and able to handle sequences of arbitrary size using the Dynamic Time Warping method (DTW).
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Submitted on : Thursday, March 29, 2018 - 11:27:43 AM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM
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  • HAL Id : tel-01748184, version 1



Georges Schutz. Adaptations et applications de modèles mixtes de réseaux de neurones à un processus industriel. Autre [cs.OH]. Université Henri Poincaré - Nancy 1, 2006. Français. ⟨NNT : 2006NAN10189⟩. ⟨tel-01748184⟩



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