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Network Stability, Realisation and Random Model Generation

Yue, Zuogong ; Thunberg, Johan LU and Goncalves, Jorge (2019) 2019 IEEE 58th Conference on Decision and Control (CDC) p.4539-4544
Abstract
Dynamical structure functions (DSFs) provide means for modelling networked dynamical systems and exploring interactive structures thereof. There have been several studies on methods/algorithms for reconstructing (Boolean) networks from time-series data. However, there are no methods currently available for random generation of DSF models with complex network structures for benchmarking. In particular, it may be desirable to generate "stable" DSF models or require the presence of feedback structures while keeping topology and dynamics random up to these constraints. This work provides procedures to obtain such models. On the path of doing so, we first study essential properties and concepts of DSF models, including realisation and... (More)
Dynamical structure functions (DSFs) provide means for modelling networked dynamical systems and exploring interactive structures thereof. There have been several studies on methods/algorithms for reconstructing (Boolean) networks from time-series data. However, there are no methods currently available for random generation of DSF models with complex network structures for benchmarking. In particular, it may be desirable to generate "stable" DSF models or require the presence of feedback structures while keeping topology and dynamics random up to these constraints. This work provides procedures to obtain such models. On the path of doing so, we first study essential properties and concepts of DSF models, including realisation and stability. Then, the paper suggests model generation algorithms, whose implementations are now publicly available. (Less)
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2019 IEEE 58th Conference on Decision and Control (CDC)
pages
4539 - 4544
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2019 IEEE 58th Conference on Decision and Control (CDC)
conference dates
2019-12-11 - 2019-12-13
external identifiers
  • scopus:85082450866
ISBN
978-1-7281-1398-2
DOI
10.1109/CDC40024.2019.9029253
language
English
LU publication?
no
id
91426e1c-b4f8-4654-b3af-d52c66645850
date added to LUP
2024-09-05 15:01:19
date last changed
2024-09-16 17:49:12
@inproceedings{91426e1c-b4f8-4654-b3af-d52c66645850,
  abstract     = {{Dynamical structure functions (DSFs) provide means for modelling networked dynamical systems and exploring interactive structures thereof. There have been several studies on methods/algorithms for reconstructing (Boolean) networks from time-series data. However, there are no methods currently available for random generation of DSF models with complex network structures for benchmarking. In particular, it may be desirable to generate "stable" DSF models or require the presence of feedback structures while keeping topology and dynamics random up to these constraints. This work provides procedures to obtain such models. On the path of doing so, we first study essential properties and concepts of DSF models, including realisation and stability. Then, the paper suggests model generation algorithms, whose implementations are now publicly available.}},
  author       = {{Yue, Zuogong and Thunberg, Johan and Goncalves, Jorge}},
  booktitle    = {{2019 IEEE 58th Conference on Decision and Control (CDC)}},
  isbn         = {{978-1-7281-1398-2}},
  language     = {{eng}},
  pages        = {{4539--4544}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Network Stability, Realisation and Random Model Generation}},
  url          = {{http://dx.doi.org/10.1109/CDC40024.2019.9029253}},
  doi          = {{10.1109/CDC40024.2019.9029253}},
  year         = {{2019}},
}