Stochastic Theory of Continuous-Time State-Space Identification
Johansson, Rolf LU
; Verhaegen, Michel
and Chou, C. T.
(1997)
36th IEEE Conference on Decision and Control, 1997
p.1866-1871
- Abstract
- Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8516984
- author
- Johansson, Rolf
LU
; Verhaegen, Michel
and Chou, C. T.
- organization
- publishing date
- 1997
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 36th IEEE Conference on Decision and Control
- pages
- 1866 - 1871
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 36th IEEE Conference on Decision and Control, 1997
- conference location
- San Diego, California, United States
- conference dates
- 1997-12-12 - 1997-12-12
- external identifiers
-
- scopus:0031370357
- DOI
- 10.1109/CDC.1997.657856
- language
- English
- LU publication?
- yes
- id
- 07badc72-c853-4be1-99c3-ffb0622fbc81 (old id 8516984)
- date added to LUP
- 2016-04-04 14:29:09
- date last changed
- 2022-01-31 18:41:26
@inproceedings{07badc72-c853-4be1-99c3-ffb0622fbc81,
abstract = {{Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem}},
author = {{Johansson, Rolf and Verhaegen, Michel and Chou, C. T.}},
booktitle = {{Proceedings of the 36th IEEE Conference on Decision and Control}},
language = {{eng}},
pages = {{1866--1871}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Stochastic Theory of Continuous-Time State-Space Identification}},
url = {{http://dx.doi.org/10.1109/CDC.1997.657856}},
doi = {{10.1109/CDC.1997.657856}},
year = {{1997}},
}