Multivariate autoregressive model applied to conditioned spectral analysis of complex systems

Risto Suoranta; Seppo Rantala

doi:10.1109/ICSYSE.1990.203130

Multivariate autoregressive model applied to conditioned spectral analysis of complex systems

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

1 Citation (Scopus)

Abstract

A method of performing out conditioned spectral analysis without the drawbacks of traditional Fourier-transform-based methods is introduced. The method is based on the estimation of the multivariate autoregressive (MAR) model. Because the MAR model is a black-box model and can describe systems with feedback loops, it is a suitable tool for the analysis of complex systems. Two different approaches for the conditioned spectral matrix in the context of the MAR model are presented. They are the reduced conditioned spectral matrix and the noise conditioned spectral matrix. These spectral quantities offer possibilities in the analysis of systems where no exact prior knowledge about internal structures is available

Original language	English
Title of host publication	1990 IEEE International Conference on Systems Engineering
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	190-193
ISBN (Print)	978-0-7803-0173-3
DOIs	https://doi.org/10.1109/ICSYSE.1990.203130
Publication status	Published - 1990
MoE publication type	A4 Article in a conference publication

Access to Document

10.1109/ICSYSE.1990.203130

Cite this

@inproceedings{3507cc4317f14b8291f65ce471a56999,

title = "Multivariate autoregressive model applied to conditioned spectral analysis of complex systems",

abstract = "A method of performing out conditioned spectral analysis without the drawbacks of traditional Fourier-transform-based methods is introduced. The method is based on the estimation of the multivariate autoregressive (MAR) model. Because the MAR model is a black-box model and can describe systems with feedback loops, it is a suitable tool for the analysis of complex systems. Two different approaches for the conditioned spectral matrix in the context of the MAR model are presented. They are the reduced conditioned spectral matrix and the noise conditioned spectral matrix. These spectral quantities offer possibilities in the analysis of systems where no exact prior knowledge about internal structures is available",

author = "Risto Suoranta and Seppo Rantala",

year = "1990",

doi = "10.1109/ICSYSE.1990.203130",

language = "English",

isbn = "978-0-7803-0173-3",

pages = "190--193",

booktitle = "1990 IEEE International Conference on Systems Engineering",

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

address = "United States",

}

Multivariate autoregressive model applied to conditioned spectral analysis of complex systems. / Suoranta, Risto; Rantala, Seppo.
1990 IEEE International Conference on Systems Engineering. IEEE Institute of Electrical and Electronic Engineers, 1990. p. 190-193.

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

TY - GEN

T1 - Multivariate autoregressive model applied to conditioned spectral analysis of complex systems

AU - Suoranta, Risto

AU - Rantala, Seppo

PY - 1990

Y1 - 1990

N2 - A method of performing out conditioned spectral analysis without the drawbacks of traditional Fourier-transform-based methods is introduced. The method is based on the estimation of the multivariate autoregressive (MAR) model. Because the MAR model is a black-box model and can describe systems with feedback loops, it is a suitable tool for the analysis of complex systems. Two different approaches for the conditioned spectral matrix in the context of the MAR model are presented. They are the reduced conditioned spectral matrix and the noise conditioned spectral matrix. These spectral quantities offer possibilities in the analysis of systems where no exact prior knowledge about internal structures is available

AB - A method of performing out conditioned spectral analysis without the drawbacks of traditional Fourier-transform-based methods is introduced. The method is based on the estimation of the multivariate autoregressive (MAR) model. Because the MAR model is a black-box model and can describe systems with feedback loops, it is a suitable tool for the analysis of complex systems. Two different approaches for the conditioned spectral matrix in the context of the MAR model are presented. They are the reduced conditioned spectral matrix and the noise conditioned spectral matrix. These spectral quantities offer possibilities in the analysis of systems where no exact prior knowledge about internal structures is available

U2 - 10.1109/ICSYSE.1990.203130

DO - 10.1109/ICSYSE.1990.203130

M3 - Conference article in proceedings

SN - 978-0-7803-0173-3

SP - 190

EP - 193

BT - 1990 IEEE International Conference on Systems Engineering

PB - IEEE Institute of Electrical and Electronic Engineers

ER -

Multivariate autoregressive model applied to conditioned spectral analysis of complex systems

Abstract

Access to Document

Fingerprint

Cite this