Online clustering of switching models based ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Online clustering of switching models based on a subspace framework
Auteur(s) :
Pekpe, Midzodzi [Auteur correspondant]
Systèmes Tolérants aux Fautes [STF]
Lecoeuche, Stéphane [Auteur]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Systèmes Tolérants aux Fautes [STF]
Lecoeuche, Stéphane [Auteur]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Titre de la revue :
Nonlinear Analysis: Hybrid Systems
Pagination :
735-749
Éditeur :
Elsevier
Date de publication :
2008-08-01
ISSN :
1751-570X
Mot(s)-clé(s) en anglais :
Identification
Markov parameters estimation
Machine learning
Dynamical classification
Switching systems
Markov parameters estimation
Machine learning
Dynamical classification
Switching systems
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Automatique / Robotique
Mathématiques [math]/Mathématiques générales [math.GM]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Mathématiques [math]/Mathématiques générales [math.GM]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
This paper deals with the modelling of switching systems and focuses on the characterization of the local functioning modes using the online clustering approach. The system considered is represented as a weighted sum of ...
Lire la suite >This paper deals with the modelling of switching systems and focuses on the characterization of the local functioning modes using the online clustering approach. The system considered is represented as a weighted sum of local linear models where each model could have its own structure. This implies that the parameters and the order of the switching system could change when the system switches. Moreover, possible constants of the local models are also unknown. The method presented consists of two steps. First, an online estimation method of the Markov parameters matrix of the local linear models is established. Secondly, the labelling of these parameters is done using a dynamical decision space worked out with learning techniques; each local model being represented by a cluster. The paper ends with an example and a discussion with an aim of illustrating the method's performance.Lire moins >
Lire la suite >This paper deals with the modelling of switching systems and focuses on the characterization of the local functioning modes using the online clustering approach. The system considered is represented as a weighted sum of local linear models where each model could have its own structure. This implies that the parameters and the order of the switching system could change when the system switches. Moreover, possible constants of the local models are also unknown. The method presented consists of two steps. First, an online estimation method of the Markov parameters matrix of the local linear models is established. Secondly, the labelling of these parameters is done using a dynamical decision space worked out with learning techniques; each local model being represented by a cluster. The paper ends with an example and a discussion with an aim of illustrating the method's performance.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://api.istex.fr/ark:/67375/6H6-PZH8X9PN-6/fulltext.pdf?sid=hal
- Accès libre
- Accéder au document
- https://api.istex.fr/ark:/67375/6H6-PZH8X9PN-6/fulltext.pdf?sid=hal
- Accès libre
- Accéder au document
- https://api.istex.fr/ark:/67375/6H6-PZH8X9PN-6/fulltext.pdf?sid=hal
- Accès libre
- Accéder au document
- https://api.istex.fr/ark:/67375/6H6-PZH8X9PN-6/fulltext.pdf?sid=hal
- Accès libre
- Accéder au document
- fulltext.pdf
- Accès libre
- Accéder au document