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 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 réseaux de neurones
Type de document :
Communication dans un congrès
IAR (german-french Institute for Automation and Robotics. 16th IAR - Annual Meeting - IAR/ICD Workshop, 2001, Strasbourg, France, pp.105-110, 2001
Liste complète des métadonnées
https://hal.inria.fr/inria-00100657
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:48:42
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

Identifiants

  • HAL Id : inria-00100657, version 1

Collections

INRIA | LORIA | UNIV-LORRAINE

Citation

Georges Schutz, Frédéric Alexandre, Serge Gillé. Neural Networks in Dynamic Process Analysis. IAR (german-french Institute for Automation and Robotics. 16th IAR - Annual Meeting - IAR/ICD Workshop, 2001, Strasbourg, France, pp.105-110, 2001. 〈inria-00100657〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

205

Contact


archive-ouverte@inria.fr