InforLorV4, Main, Exploration, bibRecord, 004F83

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

Identifieur interne : 004F83 ( Main/Exploration ); précédent : 004F82; suivant : 004F84

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

Auteurs : Georges Schutz [France]

Source :

RBID : Hal:tel-00115770

Descripteurs français

mix :
- apprentissage non-supervisé, carte auto-organisatrice, connexionnisme, dynamic time warping DTW, réseaux de neurones, similarité }.

English descriptors

mix :
- connectionism, dynamic time warping, neural networks, self organising maps, similarity, unsupervised learning.

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.

Url:

https://tel.archives-ouvertes.fr/tel-00115770

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 005559
to stream Hal, to step Curation: 005559
to stream Hal, to step Checkpoint: 003F03
to stream Main, to step Merge: 005148
to stream Main, to step Curation: 004F83

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.

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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.

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

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

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri