Utilizing multivariate autoregressive model to reveal internal dependences in multichannel measurement data

Risto Suoranta; Seppo Rantala

doi:10.1109/IMTC.1991.161603

Utilizing multivariate autoregressive model to reveal internal dependences in multichannel measurement data

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (Scopus)

Abstract

A method based on the estimation of the multivariate autoregressive model (MAR-model) is introduced to analyze multichannel data. Because the MAR-model is a black-box model and can describe systems with feedback-loops, it table for the analysis of complex closed-loop multivariate systems. The authors identify the MAR-model and, based on the model, decompose the multichannel spectral matrix. The proposed method offers a new possibility to analyze systems of which there is no exact prior knowledge of internal structures.

Original language	English
Title of host publication	IEEE Instrumentation and Measurement Technology Conference
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	315-318
ISBN (Print)	0-87942-579-2
DOIs	https://doi.org/10.1109/IMTC.1991.161603
Publication status	Published - 1991
MoE publication type	A4 Article in a conference publication

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10.1109/IMTC.1991.161603

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title = "Utilizing multivariate autoregressive model to reveal internal dependences in multichannel measurement data",

abstract = "A method based on the estimation of the multivariate autoregressive model (MAR-model) is introduced to analyze multichannel data. Because the MAR-model is a black-box model and can describe systems with feedback-loops, it table for the analysis of complex closed-loop multivariate systems. The authors identify the MAR-model and, based on the model, decompose the multichannel spectral matrix. The proposed method offers a new possibility to analyze systems of which there is no exact prior knowledge of internal structures.",

author = "Risto Suoranta and Seppo Rantala",

year = "1991",

doi = "10.1109/IMTC.1991.161603",

language = "English",

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pages = "315--318",

booktitle = "IEEE Instrumentation and Measurement Technology Conference",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

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Utilizing multivariate autoregressive model to reveal internal dependences in multichannel measurement data. / Suoranta, Risto; Rantala, Seppo.

IEEE Instrumentation and Measurement Technology Conference. IEEE Institute of Electrical and Electronic Engineers, 1991. p. 315-318.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Rantala, Seppo

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N2 - A method based on the estimation of the multivariate autoregressive model (MAR-model) is introduced to analyze multichannel data. Because the MAR-model is a black-box model and can describe systems with feedback-loops, it table for the analysis of complex closed-loop multivariate systems. The authors identify the MAR-model and, based on the model, decompose the multichannel spectral matrix. The proposed method offers a new possibility to analyze systems of which there is no exact prior knowledge of internal structures.

AB - A method based on the estimation of the multivariate autoregressive model (MAR-model) is introduced to analyze multichannel data. Because the MAR-model is a black-box model and can describe systems with feedback-loops, it table for the analysis of complex closed-loop multivariate systems. The authors identify the MAR-model and, based on the model, decompose the multichannel spectral matrix. The proposed method offers a new possibility to analyze systems of which there is no exact prior knowledge of internal structures.

U2 - 10.1109/IMTC.1991.161603

DO - 10.1109/IMTC.1991.161603

M3 - Conference article in proceedings

SN - 0-87942-579-2

SP - 315

EP - 318

BT - IEEE Instrumentation and Measurement Technology Conference

PB - IEEE Institute of Electrical and Electronic Engineers

ER -

Utilizing multivariate autoregressive model to reveal internal dependences in multichannel measurement data

Abstract

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