Balanced parametrization in time-series identification
Balanced parametrization in time-series identification
The authors examine the identification of time-series, following a predictor-error approach using a novel parametrization for predictors. This parametrization relies on a canonical form for minimum-phase systems which utilizes a generalized notion of a balanced realization. In this framework an identification scheme involving only unconstrained optimizations is developed, and an approach to the order selection problem is suggested.
3202-3203
McGinnie, B. Paul
1deb4de6-0b43-4f98-9812-a07c6465d038
Ober, Raimund J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36
Maciejowski, Jan M.
1b5c91d3-1700-465f-91a7-99cfcbf9e463
1990
McGinnie, B. Paul
1deb4de6-0b43-4f98-9812-a07c6465d038
Ober, Raimund J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36
Maciejowski, Jan M.
1b5c91d3-1700-465f-91a7-99cfcbf9e463
McGinnie, B. Paul, Ober, Raimund J. and Maciejowski, Jan M.
(1990)
Balanced parametrization in time-series identification.
In 29th IEEE Conference on Decision and Control.
vol. 6,
IEEE.
.
(doi:10.1109/CDC.1990.203380).
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Conference or Workshop Item
(Paper)
Abstract
The authors examine the identification of time-series, following a predictor-error approach using a novel parametrization for predictors. This parametrization relies on a canonical form for minimum-phase systems which utilizes a generalized notion of a balanced realization. In this framework an identification scheme involving only unconstrained optimizations is developed, and an approach to the order selection problem is suggested.
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Published date: 1990
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Local EPrints ID: 424996
URI: http://eprints.soton.ac.uk/id/eprint/424996
PURE UUID: c748400e-14d3-4228-b98b-c532a6e0bf23
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Date deposited: 09 Oct 2018 16:30
Last modified: 16 Mar 2024 04:37
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Author:
B. Paul McGinnie
Author:
Jan M. Maciejowski
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