Subspace Identification Incorporating Prior Information

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F07%3A00133872" target="_blank" >RIV/68407700:21230/07:00133872 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Subspace Identification Incorporating Prior Information

Popis výsledku v původním jazyce

Subspace identification methods proved to be a powerful tool, which can further benefit from the incorporation of prior information. In the industrial environment, there is often strong prior information about the identified system, that can be used to improve the model quality and its compliance with physical reality. Such prior information can be the known static gains, the dominant time constants, the impulse response smoothness, etc. An idea comes from the possibility to consider the subspace identification as an optimization problem of finding a model with the optimal multi-step predictions on the experimental data. Further, the problem is reformulated to the Bayesian framework allowing to combine available prior information with the information contained in the experimental data by covariance matrix shaping. The paper is completed with an application to experimental data from an oil firing steam boiler with the rated effective power of 100 MW.

Název v anglickém jazyce

Subspace Identification Incorporating Prior Information

Popis výsledku anglicky

Subspace identification methods proved to be a powerful tool, which can further benefit from the incorporation of prior information. In the industrial environment, there is often strong prior information about the identified system, that can be used to improve the model quality and its compliance with physical reality. Such prior information can be the known static gains, the dominant time constants, the impulse response smoothness, etc. An idea comes from the possibility to consider the subspace identification as an optimization problem of finding a model with the optimal multi-step predictions on the experimental data. Further, the problem is reformulated to the Bayesian framework allowing to combine available prior information with the information contained in the experimental data by covariance matrix shaping. The paper is completed with an application to experimental data from an oil firing steam boiler with the rated effective power of 100 MW.

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GA102%2F05%2F2075" target="_blank" >GA102/05/2075: Teoretické základy pro integrované řízení a optimalizaci technologických procesů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2007

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

46th IEEE Conference on Decision and Control (CDC), 2007

ISBN

978-1-4244-1497-0

ISSN

0191-2216

e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

New Orleans, Louisiana

Datum konání akce

12. 12. 2007

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000255181700233