Learning Local Modules in Dynamic Networks
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
Text
Van den Hof and Ramaswamy. "Learning Local Modules in Dynamic Networks." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Van den Hof and Ramaswamy. "Learning Local Modules in Dynamic Networks." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/vandenhof2021l4dc-learning/)BibTeX
@inproceedings{vandenhof2021l4dc-learning,
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},
year = {2021},
pages = {176-188},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/vandenhof2021l4dc-learning/}
}