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

Research output: Contribution to journal › Conference article › Academic › peer-review

19 Citations (Scopus)

201 Downloads (Pure)

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.

Original language	English
Pages (from-to)	1175-1181
Number of pages	7
Journal	IFAC-PapersOnLine
Volume	53
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2020.12.1329
Publication status	Published - Nov 2020
Event	21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 Conference number: 21 https://www.ifac2020.org/

Keywords

Model structure
Bias/variance trade-off
Model validation
LSTM
Cascadeforwardnet
Deep nets
Bias/Variance Trade-off

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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",

author = "Lennart Ljung and Carl Andersson and Koen Tiels and Sch{\"o}n, {Thomas B.}",

year = "2020",

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language = "English",

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journal = "IFAC-PapersOnLine",

issn = "2405-8963",

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TY - JOUR

T1 - Deep learning and system identification

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

M3 - Conference article

VL - 53

SP - 1175

EP - 1181

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 2

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

Y2 - 12 July 2020 through 17 July 2020

ER -

Deep learning and system identification

Abstract

Keywords

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