Linear Dynamic Network Reconstruction from Heterogeneous Datasets
(2017) 20th IFAC World Congress, 2017 In IFAC-PapersOnLine 50(1). p.10586-10591- Abstract
This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiments subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all experiments. The ARMAX model is adopted to parameterize the general linear dynamic network representation “Dynamical Structure Function” (DSF), which provides the Granger Causality graph as a special case. The network identification is performed by integrating all available datasets and promote group sparsity to assure both network sparsity and the... (More)
This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiments subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all experiments. The ARMAX model is adopted to parameterize the general linear dynamic network representation “Dynamical Structure Function” (DSF), which provides the Granger Causality graph as a special case. The network identification is performed by integrating all available datasets and promote group sparsity to assure both network sparsity and the consistency of Boolean structures over datasets. In terms of solving the problem, a treatment by the iterative reweighted l1 method is used, together with its implementations via proximal methods and ADMM for large-dimensional networks.
(Less)
- author
- Yue, Zuogong ; Thunberg, Johan LU ; Pan, Wei ; Ljung, Lennart and Gonçalves, Jorge
- publishing date
- 2017-07
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- dynamic network reconstruction, heterogeneous datasets, system identification
- host publication
- 20th IFAC World Congress
- series title
- IFAC-PapersOnLine
- volume
- 50
- issue
- 1
- pages
- 6 pages
- publisher
- Elsevier
- conference name
- 20th IFAC World Congress, 2017
- conference location
- Toulouse, France
- conference dates
- 2017-07-09 - 2017-07-14
- external identifiers
-
- scopus:85031783075
- ISSN
- 2405-8963
- DOI
- 10.1016/j.ifacol.2017.08.1314
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2017
- id
- 26c58565-d12d-4a7e-80c4-9c2bba972887
- date added to LUP
- 2024-09-05 12:32:12
- date last changed
- 2025-04-04 14:18:45
@inproceedings{26c58565-d12d-4a7e-80c4-9c2bba972887,
abstract = {{<p>This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiments subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all experiments. The ARMAX model is adopted to parameterize the general linear dynamic network representation “Dynamical Structure Function” (DSF), which provides the Granger Causality graph as a special case. The network identification is performed by integrating all available datasets and promote group sparsity to assure both network sparsity and the consistency of Boolean structures over datasets. In terms of solving the problem, a treatment by the iterative reweighted l<sub>1</sub> method is used, together with its implementations via proximal methods and ADMM for large-dimensional networks.</p>}},
author = {{Yue, Zuogong and Thunberg, Johan and Pan, Wei and Ljung, Lennart and Gonçalves, Jorge}},
booktitle = {{20th IFAC World Congress}},
issn = {{2405-8963}},
keywords = {{dynamic network reconstruction; heterogeneous datasets; system identification}},
language = {{eng}},
number = {{1}},
pages = {{10586--10591}},
publisher = {{Elsevier}},
series = {{IFAC-PapersOnLine}},
title = {{Linear Dynamic Network Reconstruction from Heterogeneous Datasets}},
url = {{http://dx.doi.org/10.1016/j.ifacol.2017.08.1314}},
doi = {{10.1016/j.ifacol.2017.08.1314}},
volume = {{50}},
year = {{2017}},
}