Identification of switched linear systems using self-adaptive SVR algorithm

We consider the problem of switched linear system identification from input-output data set. This set may be a mixte set whose data are generated from a different switching affine subsystems so that one does not know a priori or a switching dynamics is unavailable. To overcome this main challenge, w...

Full description

Saved in:

Bibliographic Details
Published in:	2016 24th Mediterranean Conference on Control and Automation (MED) pp. 617 - 621
Main Authors:	Sellami, Lamaa, Zidi, Salah, Abderrahim, Kamel
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2016
Subjects:	Clustering algorithms Heuristic algorithms Kernel Linear systems Mathematical model Support vector machines Switches
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	We consider the problem of switched linear system identification from input-output data set. This set may be a mixte set whose data are generated from a different switching affine subsystems so that one does not know a priori or a switching dynamics is unavailable. To overcome this main challenge, we develop an identification approach which consists in determining simultaneously a linear regression function which represents each submodel and a switching signal estimation via a self-adaptive clustering algorithm. The regression function is identified based on the Support Regression Vector (SVR) approach. However, the switching signal is provided by an unsupervised classification algorithm with self-adaptive capacities.
DOI:	10.1109/MED.2016.7535953