Application of the particle filters for identification of the non-Gaussian systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU114705" target="_blank" >RIV/00216305:26220/15:PU114705 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7145089" target="_blank" >https://ieeexplore.ieee.org/document/7145089</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CarpathianCC.2015.7145089" target="_blank" >10.1109/CarpathianCC.2015.7145089</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of the particle filters for identification of the non-Gaussian systems

Popis výsledku v původním jazyce

This paper focuses on application of a particle filter for online identification of non-Gaussian systems. Firstly, the Bayesian inference was described and then the particle filter was defined. The particle filter numerically solves a problem of a recursive Bayesian state estimator. Secondly, the parameters of the linear system and two types of the non-Gaussian systems were estimated by application of the particle filter. The first system was classical linear system. The second system was the linear system with a noise which had a different probability distribution than the Gaussian distribution and the last system was the system with nonlinearity. Thirdly, the parameters of the non-Gaussian systems were estimated with the gradient based method Leveberg-Marquardt. Finally, the results from the particle filter were compared with the results from the gradient based method Levenberg-Marquardt.

Název v anglickém jazyce

Application of the particle filters for identification of the non-Gaussian systems

Popis výsledku anglicky

This paper focuses on application of a particle filter for online identification of non-Gaussian systems. Firstly, the Bayesian inference was described and then the particle filter was defined. The particle filter numerically solves a problem of a recursive Bayesian state estimator. Secondly, the parameters of the linear system and two types of the non-Gaussian systems were estimated by application of the particle filter. The first system was classical linear system. The second system was the linear system with a noise which had a different probability distribution than the Gaussian distribution and the last system was the system with nonlinearity. Thirdly, the parameters of the non-Gaussian systems were estimated with the gradient based method Leveberg-Marquardt. Finally, the results from the particle filter were compared with the results from the gradient based method Levenberg-Marquardt.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 16th International Carpathian Control Conference (ICCC2015)

ISBN

978-1-4799-7369-9

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

282-285

Název nakladatele

University of Miskolc, Hungary

Místo vydání

Szilvásvárad

Místo konání akce

Szilvásvárad

Datum konání akce

27. 5. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000380488000055