Estimation of parameters of one-step predictor with particle filter method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU114708" target="_blank" >RIV/00216305:26220/15:PU114708 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2405896315008186" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896315008186</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2015.07.043" target="_blank" >10.1016/j.ifacol.2015.07.043</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of parameters of one-step predictor with particle filter method

Popis výsledku v původním jazyce

This paper is focused on estimation of the parameters of a system with non-Gaussian noise. Firstly, the Bayesian inference is described and the method of the particle filters is introduced which is directly based on the Bayesian inference. The particle filters method numrically solve a problem of a recursive Bayesian state estimator. Secondly, the method for transformation of a random variables is introduced which changes the relative likelihood of the particle filters according to the distribution of the measurement noise. Thirdly, recursive least square method is derived and linear one-step predictor is described. Fourthly, parameters of the one-step predictor are estimated online with two methods that were mention before. The outputs of both methods are compared and results are discussed. The particle filters method with random variables is analyzed.

Název v anglickém jazyce

Estimation of parameters of one-step predictor with particle filter method

Popis výsledku anglicky

This paper is focused on estimation of the parameters of a system with non-Gaussian noise. Firstly, the Bayesian inference is described and the method of the particle filters is introduced which is directly based on the Bayesian inference. The particle filters method numrically solve a problem of a recursive Bayesian state estimator. Secondly, the method for transformation of a random variables is introduced which changes the relative likelihood of the particle filters according to the distribution of the measurement noise. Thirdly, recursive least square method is derived and linear one-step predictor is described. Fourthly, parameters of the one-step predictor are estimated online with two methods that were mention before. The outputs of both methods are compared and results are discussed. The particle filters method with random variables is analyzed.

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

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

13th IFAC Conference on Programmable Devices and Embedded Systems - PDeS 2015

ISBN

—

ISSN

1474-6670

e-ISSN

—

Počet stran výsledku

6

Strana od-do

256-261

Název nakladatele

Silesian University of Technology, Poland

Místo vydání

Cracow, Poland

Místo konání akce

Krakow

Datum konání akce

13. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000375804500044