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2019 (English)In: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, p. 3670-3676Conference paper, Published paper (Refereed)
Abstract [en]
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.
Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-397528 (URN)10.1109/CDC40024.2019.9030219 (DOI)000560779003058 ()978-1-7281-1398-2 (ISBN)
Conference
CDC 2019, December 11–13, Nice, France
Funder
Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2016-06079
2020-03-122019-11-212022-04-04Bibliographically approved