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UID:20250811T012551EDT-5258zwxpx1@132.216.98.100
DTSTAMP:20250811T052551Z
DESCRIPTION:Title: Regularization Techniques in Koopman-based System Identi
 fication.\n\nAbstract: Using the Koopman operator\, nonlinear systems can 
 be expressed as infinite-dimensional linear systems. Data-driven methods c
 an then be used to approximate a finite-dimensional Koopman operator\, whi
 ch is particularly useful for system identification\, control\, and state 
 estimation tasks. However\, approximating large Koopman operators is numer
 ically challenging\, leading to unstable Koopman operators being identifie
 d for otherwise stable systems. This talk will present a selection of tech
 niques to regularize the Koopman regression problem\, including a novel H-
 infinity norm regularizer. In particular\, how to re-pose the system ident
 ification problem in order to leverage numerically efficient optimization 
 tools\, such as linear matrix inequalities\, will be presented. This talk 
 is based on the pre-print arxiv.\n\nhttps://dms.umontreal.ca/~mathapp/inde
 x_fr.html\n\n \n\n \n\nSeminar Applied Mathematics/Mathématiques appliquée
 s\n	Zoom/ Please contact : damien.tageddine [at] mail.mcgill.ca\n
DTSTART:20220912T200000Z
DTEND:20220912T210000Z
SUMMARY:James Forbes\, 9IÖÆ×÷³§Ãâ·Ñ
URL:/mathstat/channels/event/james-forbes-mcgill-34176
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