Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86088506" target="_blank" >RIV/61989100:27240/13:86088506 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/13:86088506

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-00542-3_51" target="_blank" >http://dx.doi.org/10.1007/978-3-319-00542-3_51</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-00542-3_51" target="_blank" >10.1007/978-3-319-00542-3_51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression

Popis výsledku v původním jazyce

This article deals with searching for dependences within the System of Measuring Stations.Weather measuring stations represent one of the most important data sources. The same could be said about stations that measure the composition of air and the levelof pollutants. Knowledge of the current state of air quality resulting from the measured values is essential for citizens, especially in areas affected by heavy industry or dense traffic. Computation of such air quality indicators depends on values obtained from measuring stations which are more or less reliable. They can have failures or they can measure just a part of the required values. In general, searching for dependences represents a complex and non-linear problem that can be effectively solvedby some class of evolutionary algorithms. This article describes a method that helps us to predict the levels of air quality in the case of station failure or data loss. The model is constructed by the symbolic regression with usage of th

Název v anglickém jazyce

Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression

Popis výsledku anglicky

This article deals with searching for dependences within the System of Measuring Stations.Weather measuring stations represent one of the most important data sources. The same could be said about stations that measure the composition of air and the levelof pollutants. Knowledge of the current state of air quality resulting from the measured values is essential for citizens, especially in areas affected by heavy industry or dense traffic. Computation of such air quality indicators depends on values obtained from measuring stations which are more or less reliable. They can have failures or they can measure just a part of the required values. In general, searching for dependences represents a complex and non-linear problem that can be effectively solvedby some class of evolutionary algorithms. This article describes a method that helps us to predict the levels of air quality in the case of station failure or data loss. The model is constructed by the symbolic regression with usage of th

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Advances in Intelligent Systems and Computing. Volume 210

ISSN

2194-5357

e-ISSN

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Svazek periodika

210

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

12

Strana od-do

517-528

Kód UT WoS článku

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EID výsledku v databázi Scopus

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