Deep learning and system identification

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

Deep learning and system identification

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

Control Systems Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 Identication 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 Identication code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.

Original language	English
Title of host publication	Deep learning and system identification
Number of pages	7
Publication status	Accepted/In press - 27 Feb 2020

Keywords

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

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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 Identication 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 Identication code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.",

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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 Identication 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 Identication 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 Identication 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 Identication code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.

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KW - Deep nets

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