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

Georges Schutz 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
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Contributor : Frédéric Alexandre <>
Submitted on : Thursday, November 23, 2006 - 11:05:06 AM
Last modification on : Thursday, February 11, 2021 - 2:48:13 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 6:59:32 PM


  • HAL Id : tel-00115770, version 1



Georges Schutz. Adaptations et applications de modèles mixtes de réseaux de neurones à un processus industriel. Interface homme-machine [cs.HC]. Université Henri Poincaré - Nancy I, 2006. Français. ⟨tel-00115770⟩



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