Online Classification of Switching Models ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Online Classification of Switching Models Based on Subspace Framework
Auteur(s) :
Pekpe, Midzodzi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Lecoeuche, Stéphane [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Lecoeuche, Stéphane [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Titre de la manifestation scientifique :
2nd IFAC Conference on Analysis and Design of Hybrid System, ADHS'06
Ville :
Alghero, Sardinia
Pays :
Italie
Date de début de la manifestation scientifique :
2006-06-07
Date de publication :
2006
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Automatique / Robotique
Résumé en anglais : [en]
The paper deals with the modelling of switching systems and focuses on the characterization of the local functioning modes using online clustering approach. The considered system is represented as a weighted sum of local ...
Lire la suite >The paper deals with the modelling of switching systems and focuses on the characterization of the local functioning modes using online clustering approach. The considered system is represented as a weighted sum of local linear models where each model could have its own structure. That implies that the parameters and the order of the switching system could change when the system switches. The presented method consists in two steps. First, an online estimation method of the Markov parameters matrix of the local linear models is established. Secondly, the labelling of theses 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, in view to illustrate the method performances.Lire moins >
Lire la suite >The paper deals with the modelling of switching systems and focuses on the characterization of the local functioning modes using online clustering approach. The considered system is represented as a weighted sum of local linear models where each model could have its own structure. That implies that the parameters and the order of the switching system could change when the system switches. The presented method consists in two steps. First, an online estimation method of the Markov parameters matrix of the local linear models is established. Secondly, the labelling of theses 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, in view to illustrate the method performances.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Oui
Collections :
Source :