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Residual Models and Stochastic Realization in State-Space System Identification

Robertsson, Anders LU ; Chou, C. T.; Verhaegen, Michel and Johansson, Rolf 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.
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author
organization
publishing date
type
Contribution to conference
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
conference name
37th Conference on Decision and Control, 1998
external identifiers
  • Scopus:0032269936
language
English
LU publication?
yes
id
6d5208c5-c4cc-4989-ac1f-e96ea60d57b3 (old id 8516896)
date added to LUP
2016-01-14 14:18:48
date last changed
2016-10-13 04:51:35
@misc{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       = {Robertsson, Anders and Chou, C. T. and Verhaegen, Michel and Johansson, Rolf},
  keyword      = {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},
  title        = {Residual Models and Stochastic Realization in State-Space System Identification},
  year         = {1998},
}