Online Classification of Switching Models ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Online Classification of Switching Models Based on Subspace Framework
Author(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]
Conference title :
2nd IFAC Conference on Analysis and Design of Hybrid System, ADHS'06
City :
Alghero, Sardinia
Country :
Italie
Start date of the conference :
2006-06-07
Publication date :
2006
HAL domain(s) :
Sciences de l'ingénieur [physics]/Automatique / Robotique
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Oui
Collections :
Source :