Multiscale Analysis of False Neighbors for State Space Reconstruction of Complicated Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F11%3A00188404" target="_blank" >RIV/68407700:21220/11:00188404 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5949517" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5949517</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MFCIST.2011.5949517" target="_blank" >10.1109/MFCIST.2011.5949517</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multiscale Analysis of False Neighbors for State Space Reconstruction of Complicated Systems

Popis výsledku v původním jazyce

This paper introduces Multiscale False Neighbors Analysis (MSFNA) as a supporting tool for state space reconstruction for real-data based modeling techniques. Common false neighbors analysis evaluates uncertainty in mapping of input data to output data for a single setup of radii that define neighborhood and whose correct definition is usually unknown. Contrary to common false neighbors analysis, MSFNA evaluates uncertainty of input-output mapping data by evaluation of false neighbors along the whole intervals of radii that results in overall characterization of uncertainty in input-output data. The power-law concept is applied to the MSFNA as a supportive technique for characterization of uncertainty in data. The proposed MSFNA is demonstrated on comparison of various estimations of state vectors of an artificial plant as well as a real power plant coal burning furnace.

Název v anglickém jazyce

Multiscale Analysis of False Neighbors for State Space Reconstruction of Complicated Systems

Popis výsledku anglicky

This paper introduces Multiscale False Neighbors Analysis (MSFNA) as a supporting tool for state space reconstruction for real-data based modeling techniques. Common false neighbors analysis evaluates uncertainty in mapping of input data to output data for a single setup of radii that define neighborhood and whose correct definition is usually unknown. Contrary to common false neighbors analysis, MSFNA evaluates uncertainty of input-output mapping data by evaluation of false neighbors along the whole intervals of radii that results in overall characterization of uncertainty in input-output data. The power-law concept is applied to the MSFNA as a supportive technique for characterization of uncertainty in data. The proposed MSFNA is demonstrated on comparison of various estimations of state vectors of an artificial plant as well as a real power plant coal burning furnace.

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

<a href="/cs/project/FR-TI1%2F538" target="_blank" >FR-TI1/538: Technologie měření pro pokročilé řízení spalovacích procesů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Merging Fields Of Computational Intelligence and Sensor Technology (CompSens), 2011 IEEE Workshop On

ISBN

978-1-4244-9912-0

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

65-72

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Paris

Datum konání akce

11. 4. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000302993800010