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System Aliasing in Dynamic Network Reconstruction:Issues on Low Sampling Frequencies

Yue, Zuogong ; Thunberg, Johan LU ; Ljung, Lennart ; Yuan, Ye and Goncalves, Jorge (2021) In IEEE Transactions on Automatic Control 66(12). p.5788-5801
Abstract

Network reconstruction of dynamical continuous-time (CT) systems is motivated by applications in many fields. Due to experimental limitations, especially in biology, data can be sampled at low frequencies, leading to significant challenges in network inference. We introduce the concept of 'system aliasing' and characterize the minimal sampling frequency that allows reconstruction of CT systems from low sampled data. A test criterion is also proposed to detect the presence of system aliasing. With no system aliasing, this article provides an algorithm to reconstruct dynamic networks from full-state measurements in the presence of noise. With system aliasing, we add additional prior information such as sparsity to overcome the lack of... (More)

Network reconstruction of dynamical continuous-time (CT) systems is motivated by applications in many fields. Due to experimental limitations, especially in biology, data can be sampled at low frequencies, leading to significant challenges in network inference. We introduce the concept of 'system aliasing' and characterize the minimal sampling frequency that allows reconstruction of CT systems from low sampled data. A test criterion is also proposed to detect the presence of system aliasing. With no system aliasing, this article provides an algorithm to reconstruct dynamic networks from full-state measurements in the presence of noise. With system aliasing, we add additional prior information such as sparsity to overcome the lack of identifiability. This article opens new directions in modeling of network systems where samples have significant costs. Such tools are essential to process available data in applications subject to experimental limitations.

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author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Continuous time systems, Linear systems, Low sampling frequency, Network reconstruction, System identification
in
IEEE Transactions on Automatic Control
volume
66
issue
12
pages
14 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85097925978
ISSN
0018-9286
DOI
10.1109/TAC.2020.3042487
language
English
LU publication?
no
additional info
Publisher Copyright: © 1963-2012 IEEE.
id
b7d0ce8f-66af-41af-b0fa-1865f26a8ff2
date added to LUP
2024-09-05 09:02:25
date last changed
2024-09-05 16:03:29
@article{b7d0ce8f-66af-41af-b0fa-1865f26a8ff2,
  abstract     = {{<p>Network reconstruction of dynamical continuous-time (CT) systems is motivated by applications in many fields. Due to experimental limitations, especially in biology, data can be sampled at low frequencies, leading to significant challenges in network inference. We introduce the concept of 'system aliasing' and characterize the minimal sampling frequency that allows reconstruction of CT systems from low sampled data. A test criterion is also proposed to detect the presence of system aliasing. With no system aliasing, this article provides an algorithm to reconstruct dynamic networks from full-state measurements in the presence of noise. With system aliasing, we add additional prior information such as sparsity to overcome the lack of identifiability. This article opens new directions in modeling of network systems where samples have significant costs. Such tools are essential to process available data in applications subject to experimental limitations.</p>}},
  author       = {{Yue, Zuogong and Thunberg, Johan and Ljung, Lennart and Yuan, Ye and Goncalves, Jorge}},
  issn         = {{0018-9286}},
  keywords     = {{Continuous time systems; Linear systems; Low sampling frequency; Network reconstruction; System identification}},
  language     = {{eng}},
  month        = {{12}},
  number       = {{12}},
  pages        = {{5788--5801}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Automatic Control}},
  title        = {{System Aliasing in Dynamic Network Reconstruction:Issues on Low Sampling Frequencies}},
  url          = {{http://dx.doi.org/10.1109/TAC.2020.3042487}},
  doi          = {{10.1109/TAC.2020.3042487}},
  volume       = {{66}},
  year         = {{2021}},
}