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Deep learning applied to system identification: A probabilistic approach
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification.

Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes.

Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.

Place, publisher, year, edition, pages
Uppsala University, 2019.
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2019-007
National Category
Signal Processing Probability Theory and Statistics
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-397563OAI: oai:DiVA.org:uu-397563DiVA, id: diva2:1372031
Supervisors
Available from: 2019-11-18 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved
List of papers
1. Data-driven impulse response regularization via deep learning
Open this publication in new window or tab >>Data-driven impulse response regularization via deep learning
2018 (English)Conference paper, Published paper (Refereed)
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-366186 (URN)10.1016/j.ifacol.2018.09.081 (DOI)000446599200002 ()
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Available from: 2018-10-08 Created: 2018-11-22 Last updated: 2022-04-04Bibliographically approved
2. Deep convolutional networks in system identification
Open this publication in new window or tab >>Deep convolutional networks in system identification
Show others...
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
Available from: 2020-03-12 Created: 2019-11-21 Last updated: 2022-04-04Bibliographically approved
3. Evaluating model calibration in classification
Open this publication in new window or tab >>Evaluating model calibration in classification
Show others...
2019 (English)In: 22nd International Conference on Artificial Intelligence and Statistics, 2019, p. 3459-3467Conference paper, Published paper (Refereed)
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-397519 (URN)000509687903053 ()
Conference
AISTATS 2019, April 16–18, Naha, Japan
Available from: 2019-04-25 Created: 2019-11-21 Last updated: 2023-04-26Bibliographically approved

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Andersson, Carl

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
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Output format
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