Regularized Switched System Identification: a Statistical Learning Perspective. Issue 5 (2021)

• Record Type: Journal Article
• Authors: Massucci, Louis; Lauer, Fabien; Gilson, Marion
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 5(2021)
• Page Start: 55
• Page End: 60
• Publication Date: 2021
• Subjects: Switched systems -- System identification -- Statistical learning -- Model selection; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.474
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Switched system identification is a challenging problem, for which many methods were proposed over the last twenty years. Despite this effort, estimating the number of modes of switched systems from input–output data remains a nontrivial and critical issue for most of these methods. This paper discusses a recently proposed statistical learning approach to deal with this issue and proposes to go one step further by considering new results dedicated to regularized models. Optimization algorithms devised to tackle the estimation of such models from data are also proposed and illustrated in a few numerical experiments.