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...

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Vydané v:	2016 24th Mediterranean Conference on Control and Automation (MED) s. 617 - 621
Hlavní autori:	Sellami, Lamaa, Zidi, Salah, Abderrahim, Kamel
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2016
Predmet:	Clustering algorithms Heuristic algorithms Kernel Linear systems Mathematical model Support vector machines Switches
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Popis
Shrnutí:	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