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Neural Networks in Dynamic Process Analysis

Georges Schutz 1 Frédéric Alexandre 1 Serge Gillé 
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This article presents a study performed within the framework of an industrial research project subsidised by the ECSC. Methods of signal encoding and analysis based on the dynamic evolution of signals coming from an industrial process are presented. They are based on self-organising maps of Kohonen, a well known neural network family for clustering. The main advantage of this approach is that the basic components used for the encoding are based on a learning algorithm, thus the encoding is well adapted to the analysed signals. An other advantage is the extensibility of the presented method to multi-variable analysis. First results are shown.
Mots-clés : process diagnosis réseaux de neurones artificial neural networks clustering self-organising maps pattern recognition time series signal analysis signal encoding artificiels classification cartes auto-organisatrices reconnaissance de formes série temporelle analyse de signaux codage de signaux diagnostic de processus
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Conference papers
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https://hal.inria.fr/inria-00100657
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Submitted on : Tuesday, September 26, 2006 - 2:48:42 PM
Last modification on : Friday, February 4, 2022 - 3:30:52 AM

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  • HAL Id : inria-00100657, version 1

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CNRS | INRIA | UNIV-LORRAINE | INRIA2 | LORIA

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Georges Schutz, Frédéric Alexandre, Serge Gillé. Neural Networks in Dynamic Process Analysis. 16th IAR - Annual Meeting - IAR/ICD Workshop, ENSPS (Ecole Nationale Supérieure de Physique de Strasbourg / ULP (Université Louis Pasteur Strasbourg), 2001, Strasbourg, France, pp.105-110. ⟨inria-00100657⟩

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