Learning local modules in dynamic networks

Paul M.J. Van den Hof, Karthik R. Ramaswamy
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:176-188, 2021.

Abstract

Over the last decade, the problem of data-driven modeling in linear dynamic networks has been introduced in the literature, and has shown to contain many different challenging research questions. The structural and topological properties of networks become a central ingredient in the data-driven modeling problem, as well as the selection of locations for signals to be sensed and for excitation signals to be added. In this survey-type paper we will present an overview of recent results that are obtained for the problem of learning the dynamics of a single link/module in a dynamic network of which the topology is given. The surveyed methods include extensions of classical identification methods, combined with Bayesian kernel-based methods. Particular attention will be given to the selection of signals that need to be available for measurement/excitation, and accuracy properties of the estimated models in terms of consistency and minimum variance properties.

Cite this Paper

BibTeX
@InProceedings{pmlr-v144-van-den-hof21a, title = {Learning local modules in dynamic networks}, author = {{Van den Hof}, Paul M.J. and Ramaswamy, Karthik R.}, booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control}, pages = {176--188}, year = {2021}, editor = {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.}, volume = {144}, series = {Proceedings of Machine Learning Research}, month = {07 -- 08 June}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v144/van-den-hof21a/van-den-hof21a.pdf}, url = {https://proceedings.mlr.press/v144/van-den-hof21a.html}, abstract = {Over the last decade, the problem of data-driven modeling in linear dynamic networks has been introduced in the literature, and has shown to contain many different challenging research questions. The structural and topological properties of networks become a central ingredient in the data-driven modeling problem, as well as the selection of locations for signals to be sensed and for excitation signals to be added. In this survey-type paper we will present an overview of recent results that are obtained for the problem of learning the dynamics of a single link/module in a dynamic network of which the topology is given. The surveyed methods include extensions of classical identification methods, combined with Bayesian kernel-based methods. Particular attention will be given to the selection of signals that need to be available for measurement/excitation, and accuracy properties of the estimated models in terms of consistency and minimum variance properties.} }
Endnote
%0 Conference Paper %T Learning local modules in dynamic networks %A Paul M.J. Van den Hof %A Karthik R. Ramaswamy %B Proceedings of the 3rd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2021 %E Ali Jadbabaie %E John Lygeros %E George J. Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire J. Tomlin %E Melanie N. Zeilinger %F pmlr-v144-van-den-hof21a %I PMLR %P 176--188 %U https://proceedings.mlr.press/v144/van-den-hof21a.html %V 144 %X Over the last decade, the problem of data-driven modeling in linear dynamic networks has been introduced in the literature, and has shown to contain many different challenging research questions. The structural and topological properties of networks become a central ingredient in the data-driven modeling problem, as well as the selection of locations for signals to be sensed and for excitation signals to be added. In this survey-type paper we will present an overview of recent results that are obtained for the problem of learning the dynamics of a single link/module in a dynamic network of which the topology is given. The surveyed methods include extensions of classical identification methods, combined with Bayesian kernel-based methods. Particular attention will be given to the selection of signals that need to be available for measurement/excitation, and accuracy properties of the estimated models in terms of consistency and minimum variance properties.
APA
Van den Hof, P.M. & Ramaswamy, K.R.. (2021). Learning local modules in dynamic networks. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:176-188 Available from https://proceedings.mlr.press/v144/van-den-hof21a.html.

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