Deep learning and system identification

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

doi:10.1016/j.ifacol.2020.12.1329

Deep learning and system identification

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

Onderzoeksoutput: Bijdrage aan tijdschrift › Congresartikel › peer review

43 Citaten (Scopus)

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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-2	Engels
Pagina's (van-tot)	1175-1181
Aantal pagina's	7
Tijdschrift	IFAC-PapersOnLine
Volume	53
Nummer van het tijdschrift	2
DOI's	https://doi.org/10.1016/j.ifacol.2020.12.1329
Status	Gepubliceerd - nov. 2020
Evenement	21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Duitsland Duur: 12 jul. 2020 → 17 jul. 2020 Congresnummer: 21 https://www.ifac2020.org/

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10.1016/j.ifacol.2020.12.1329

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title = "Deep learning and system identification",

abstract = "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.",

keywords = "Model structure, Bias/variance trade-off, Model validation, LSTM, Cascadeforwardnet, Deep nets, Bias/Variance Trade-off",

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AU - Ljung, Lennart

AU - Andersson, Carl

AU - Tiels, Koen

AU - Schön, Thomas B.

N1 - Conference code: 21

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

KW - Model structure

KW - Bias/variance trade-off

KW - Model validation

KW - LSTM

KW - Cascadeforwardnet

KW - Deep nets

KW - Bias/Variance Trade-off

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U2 - 10.1016/j.ifacol.2020.12.1329

DO - 10.1016/j.ifacol.2020.12.1329

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VL - 53

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EP - 1181

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T2 - 21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress)

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Deep learning and system identification

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