On dynamic network modeling of stationary multivariate processes

Research output: Contribution to journalConference articleAcademicpeer-review

Abstract

We study the modeling of a stationary multivariate stochastic process as the output of a dynamic network driven by white noise. When this noise corresponds to the innovation, i.e. the unpredictable part of the process, we show that the network satisfies certain stability conditions. Restricting the network model to having diagonal noise structure, we show that the innovation-driven representation is unique and internally stable. We provide a one-to-one correspondence between this representation and the spectral factor associated with the innovation model. For two-node networks, we show that a representation with diagonal noise model can be obtained from a generic one through an explicit map.

LanguageEnglish
Pages850-855
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number15
DOIs
StatePublished - 8 Oct 2018
Event18th IFAC Symposium on System Identification (SYSID 2018) - Stockholm, Sweden
Duration: 9 Jul 201811 Jul 2018

Fingerprint

Innovation
White noise
Random processes

Cite this

@article{88dae85dd51d4324a9a6d04caf2b900e,
title = "On dynamic network modeling of stationary multivariate processes",
abstract = "We study the modeling of a stationary multivariate stochastic process as the output of a dynamic network driven by white noise. When this noise corresponds to the innovation, i.e. the unpredictable part of the process, we show that the network satisfies certain stability conditions. Restricting the network model to having diagonal noise structure, we show that the innovation-driven representation is unique and internally stable. We provide a one-to-one correspondence between this representation and the spectral factor associated with the innovation model. For two-node networks, we show that a representation with diagonal noise model can be obtained from a generic one through an explicit map.",
author = "Giulio Bottegal and Alessandro Chiuso and {Van den Hof}, {Paul M.J.}",
year = "2018",
month = "10",
day = "8",
doi = "10.1016/j.ifacol.2018.09.118",
language = "English",
volume = "51",
pages = "850--855",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "Elsevier",
number = "15",

}

On dynamic network modeling of stationary multivariate processes. / Bottegal, Giulio; Chiuso, Alessandro; Van den Hof, Paul M.J.

In: IFAC-PapersOnLine, Vol. 51, No. 15, 08.10.2018, p. 850-855.

Research output: Contribution to journalConference articleAcademicpeer-review

TY - JOUR

T1 - On dynamic network modeling of stationary multivariate processes

AU - Bottegal,Giulio

AU - Chiuso,Alessandro

AU - Van den Hof,Paul M.J.

PY - 2018/10/8

Y1 - 2018/10/8

N2 - We study the modeling of a stationary multivariate stochastic process as the output of a dynamic network driven by white noise. When this noise corresponds to the innovation, i.e. the unpredictable part of the process, we show that the network satisfies certain stability conditions. Restricting the network model to having diagonal noise structure, we show that the innovation-driven representation is unique and internally stable. We provide a one-to-one correspondence between this representation and the spectral factor associated with the innovation model. For two-node networks, we show that a representation with diagonal noise model can be obtained from a generic one through an explicit map.

AB - We study the modeling of a stationary multivariate stochastic process as the output of a dynamic network driven by white noise. When this noise corresponds to the innovation, i.e. the unpredictable part of the process, we show that the network satisfies certain stability conditions. Restricting the network model to having diagonal noise structure, we show that the innovation-driven representation is unique and internally stable. We provide a one-to-one correspondence between this representation and the spectral factor associated with the innovation model. For two-node networks, we show that a representation with diagonal noise model can be obtained from a generic one through an explicit map.

UR - http://www.scopus.com/inward/record.url?scp=85054384185&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2018.09.118

DO - 10.1016/j.ifacol.2018.09.118

M3 - Conference article

VL - 51

SP - 850

EP - 855

JO - IFAC-PapersOnLine

T2 - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 15

ER -