Adaptive Estimation of OEMA Model via Variable Regularized Recursive Least Squares Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F12%3APU98917" target="_blank" >RIV/00216305:26220/12:PU98917 - 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

Adaptive Estimation of OEMA Model via Variable Regularized Recursive Least Squares Method

Popis výsledku v původním jazyce

In this paper, we present a recursive least squares method with variable regularization, which is in order to track time varying parameters extended by an exponential forgetting factor (EF-VR-RLS). Regularization of a direction matrix is achieved by adding restricting conditions into the cost function. By definition of the standard recursive least-squares method (RLS) implies that the method removes the ambiguity of the cost function using a constant regularization. The RLS method is therefore an exactformulation of multi-criteria problem. In this case regularization element is the initial value of the direction matrix, which penalizes Euclidean distance between the estimated parameters and their initial values. At the proposed approach the cost function is extended by the penalization of weighted difference between the investigated vector of parameters and its currently available estimate. By introducing the element of variable regularization is possible to better effect the rate of

Název v anglickém jazyce

Adaptive Estimation of OEMA Model via Variable Regularized Recursive Least Squares Method

Popis výsledku anglicky

In this paper, we present a recursive least squares method with variable regularization, which is in order to track time varying parameters extended by an exponential forgetting factor (EF-VR-RLS). Regularization of a direction matrix is achieved by adding restricting conditions into the cost function. By definition of the standard recursive least-squares method (RLS) implies that the method removes the ambiguity of the cost function using a constant regularization. The RLS method is therefore an exactformulation of multi-criteria problem. In this case regularization element is the initial value of the direction matrix, which penalizes Euclidean distance between the estimated parameters and their initial values. At the proposed approach the cost function is extended by the penalization of weighted difference between the investigated vector of parameters and its currently available estimate. By introducing the element of variable regularization is possible to better effect the rate of

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GD102%2F09%2FH081" target="_blank" >GD102/09/H081: SYNERGIE - Mobilní senzorické systémy a sítě</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 11th IFAC/IEEE INTERNATIONAL CONFERENCE on PROGRAMMABLE DEVICES and EMBEDDED SYSTEMS PDeS 2012

ISBN

978-3-902823-21-2

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

76-79

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Brno

Datum konání akce

23. 5. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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