Deep learning and system identification

Lennart Ljung, Carl Andersson, Koen Tiels, Thomas B. Schön

Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelAcademicpeer review

7 Citaten (Scopus)
158 Downloads (Pure)

Samenvatting

Deep learning is a topic of considerable interest today. Since it deals with estimating - or learning - models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are explored and exploited in this contribution. It is stressed that common deep nets such as feedforward and cascadeforward nets are nonlinear ARX (NARX) models, and can thus be easily incorporated in System Identification code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.

Originele taal-2Engels
Pagina's (van-tot)1175-1181
Aantal pagina's7
TijdschriftIFAC-PapersOnLine
Volume53
Nummer van het tijdschrift2
DOI's
StatusGepubliceerd - nov 2020
Evenement21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Duitsland
Duur: 12 jul 202017 jul 2020
Congresnummer: 21
https://www.ifac2020.org/

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