?Aladin? weather model local revisions using the differential polynomial neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86090322" target="_blank" >RIV/61989100:27740/14:86090322 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.14311/NNW.2014.24.009" target="_blank" >http://dx.doi.org/10.14311/NNW.2014.24.009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2014.24.009" target="_blank" >10.14311/NNW.2014.24.009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

?Aladin? weather model local revisions using the differential polynomial neural network

Popis výsledku v původním jazyce

The 48-hour ?Aladin? forecast model can predict significant meteorological quantities in a middle scale area. Neural networks could try to replace some statistical techniques designed to adapt a global meteorological numerical forecast model for local conditions, described with real data surface observations. They succeed commonly a cut above problem solutions with a predefined testing data set, which provides bearing inputs for a trained model. Time-series predictions of the very complex and dynamic weather system are sophisticated and not any time faithful using simple neural network models entered only some few variables of their own next-time step estimations. Predicted values of a global meteorological forecast might instead enter a neural networklocally trained model, for refine it. Differential polynomial neural network is a new neural network type developed by the author; it constructs and substitutes for an unknown general sum partial differential equation of a system descrip

Název v anglickém jazyce

?Aladin? weather model local revisions using the differential polynomial neural network

Popis výsledku anglicky

The 48-hour ?Aladin? forecast model can predict significant meteorological quantities in a middle scale area. Neural networks could try to replace some statistical techniques designed to adapt a global meteorological numerical forecast model for local conditions, described with real data surface observations. They succeed commonly a cut above problem solutions with a predefined testing data set, which provides bearing inputs for a trained model. Time-series predictions of the very complex and dynamic weather system are sophisticated and not any time faithful using simple neural network models entered only some few variables of their own next-time step estimations. Predicted values of a global meteorological forecast might instead enter a neural networklocally trained model, for refine it. Differential polynomial neural network is a new neural network type developed by the author; it constructs and substitutes for an unknown general sum partial differential equation of a system descrip

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

2014

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

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Svazek periodika

Vol.24

Číslo periodika v rámci svazku

No.2/2014

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

14

Strana od-do

143-156

Kód UT WoS článku

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EID výsledku v databázi Scopus

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