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Residual Models and Stochastic Realization in State-Space System Identification

Johansson, Rolf LU orcid ; Chou, C. T. ; Verhaegen, Michel and Robertsson, Anders LU (1998) 37th Conference on Decision and Control, 1998
Abstract
This paper presents theory and algorithms for validation in system identification of state-space models from finite input-output sequences in a subspace model identification framework. Similar to the case of prediction-error identification, it is shown that 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:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
residual models, stochastic realization, state-space system identification, finite input-output sequences, I/O sequences, subspace model identification, model decomposition, stochastic innovations model, Riccati equation, reduced-order stochastic
host publication
Proceedings of the 37th IEEE Conference on Decision and Control
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
37th Conference on Decision and Control, 1998
conference location
Tampa, Florida, United States
conference dates
1998-12-18 - 1998-12-18
external identifiers
  • scopus:0032269936
DOI
10.1109/CDC.1998.758237
language
English
LU publication?
yes
id
6d5208c5-c4cc-4989-ac1f-e96ea60d57b3 (old id 8516896)
date added to LUP
2016-04-04 12:50:06
date last changed
2022-01-31 18:41:26
@inproceedings{6d5208c5-c4cc-4989-ac1f-e96ea60d57b3,
  abstract     = {{This paper presents theory and algorithms for validation in system identification of state-space models from finite input-output sequences in a subspace model identification framework. Similar to the case of prediction-error identification, it is shown that 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 Chou, C. T. and Verhaegen, Michel and Robertsson, Anders}},
  booktitle    = {{Proceedings of the 37th IEEE Conference on Decision and Control}},
  keywords     = {{residual models; stochastic realization; state-space system identification; finite input-output sequences; I/O sequences; subspace model identification; model decomposition; stochastic innovations model; Riccati equation; reduced-order stochastic}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Residual Models and Stochastic Realization in State-Space System Identification}},
  url          = {{http://dx.doi.org/10.1109/CDC.1998.758237}},
  doi          = {{10.1109/CDC.1998.758237}},
  year         = {{1998}},
}