Parameter Estimation in Linear Dynamic Systems using Bayesian networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F19%3A00006706" target="_blank" >RIV/46747885:24210/19:00006706 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24220/19:00006706

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8815029" target="_blank" >https://ieeexplore.ieee.org/document/8815029</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PC.2019.8815029" target="_blank" >10.1109/PC.2019.8815029</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parameter Estimation in Linear Dynamic Systems using Bayesian networks

Popis výsledku v původním jazyce

Parameter estimation in models of dynamic systems is a common preliminary procedure in control, monitoring, fault detection and diagnosis. A range of tools to fulfill this task has been constantly expanded with new additions to meet the increasing demands of modern technological processes. This article proposes the use of Bayesian networks to estimate the parameters of linear dynamic systems described by a state-space model. The proposed approach is used for system identification of two simulated dynamic systems. The parameters are estimated using a learning function for Bayesian networks from Bayes Net Toolbox for Matlab. The results indicate that Bayesian networks can be used as a system identification tool that can compete with conventional methods. However, the approach requires further research aiming to increase convergence of estimates and eliminate numerical problems.

Název v anglickém jazyce

Parameter Estimation in Linear Dynamic Systems using Bayesian networks

Popis výsledku anglicky

Parameter estimation in models of dynamic systems is a common preliminary procedure in control, monitoring, fault detection and diagnosis. A range of tools to fulfill this task has been constantly expanded with new additions to meet the increasing demands of modern technological processes. This article proposes the use of Bayesian networks to estimate the parameters of linear dynamic systems described by a state-space model. The proposed approach is used for system identification of two simulated dynamic systems. The parameters are estimated using a learning function for Bayesian networks from Bayes Net Toolbox for Matlab. The results indicate that Bayesian networks can be used as a system identification tool that can compete with conventional methods. However, the approach requires further research aiming to increase convergence of estimates and eliminate numerical problems.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2019 22nd International Conference on Process Control

ISBN

978-1-72813-758-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

203-208

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Štrbské Pleso

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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