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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Bibliographic Details
Published in2016 24th Mediterranean Conference on Control and Automation (MED) pp. 617 - 621
Main Authors Sellami, Lamaa, Zidi, Salah, Abderrahim, Kamel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Clustering algorithms
Heuristic algorithms
Kernel
Linear systems
Mathematical model
Support vector machines
Switches
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DOI10.1109/MED.2016.7535953

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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