14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrB1.6

Lecoeuche, Stéphane (Mines de Douai), Mercère, Guillaume (Univ. de Poitiers), Amadou-Boubacar, Habiboulaye (Univ. de Lille)

Modelling of Non Stationary Systems Based on a Dynamical Decision Space

Scheduled for presentation during the Regular Session "Kernel Based Nonlinear System Identification" (FrB1), Friday, March 31, 2006, 17:10−17:30, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Machine Learning and Data Mining, Recursive Identification, Hybrid and Distributed System Identification


A new approach based on pattern recognition techniques and dedicated to the monitoring of non stationary systems is presented in this paper. More precisely, it consists of a recursive subspace identification algorithm combined with an adaptive classifier set for non stationary environment. The system identification method which provides a recursive estimation of a linear state space model is firstly described. Then, a feature vector representing the system functioning state is extracted from this estimated model. Next, the dynamical clustering algorithm which online learns the functioning modes and continuously determines the current mode of the system is introduced. Its auto adaptive and unsupervised abilities to take into account system modes evolutions are finally emphasized on simulation examples.