Continuous-Time Model Identification Using Non-Uniformly Sampled Data
Johansson, Rolf LU
; Cescon, Marzia
LU
and Ståhl, Fredrik
LU
(2013)
IEEE AFRICON 2013 Conference
- Abstract
- This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based 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... (More)
- This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based 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. Next, theory, algorithms and validation results are presented for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4076221
- author
- Johansson, Rolf
LU
; Cescon, Marzia
LU
and Ståhl, Fredrik
LU
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proc. IEEE AFRICON 2013 Conference
- conference name
- IEEE AFRICON 2013 Conference
- conference location
- Mauritius
- conference dates
- 2013-09-09 - 2013-09-12
- external identifiers
-
- scopus:84897426161
- language
- English
- LU publication?
- yes
- id
- d09ea4fe-b915-48dd-aae0-76110ed4e160 (old id 4076221)
- date added to LUP
- 2016-04-04 13:31:25
- date last changed
- 2022-08-17 15:09:53
@inproceedings{d09ea4fe-b915-48dd-aae0-76110ed4e160,
abstract = {{This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based 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. Next, theory, algorithms and validation results are presented for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data.}},
author = {{Johansson, Rolf and Cescon, Marzia and Ståhl, Fredrik}},
booktitle = {{Proc. IEEE AFRICON 2013 Conference}},
language = {{eng}},
title = {{Continuous-Time Model Identification Using Non-Uniformly Sampled Data}},
year = {{2013}},
}