Forecast Models of Partial Differential Equations using Polynomial Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86087575" target="_blank" >RIV/61989100:27740/13:86087575 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-01796-9_1" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01796-9_1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01796-9_1" target="_blank" >10.1007/978-3-319-01796-9_1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecast Models of Partial Differential Equations using Polynomial Networks

Popis výsledku v původním jazyce

Unknown data relations can describe lots of complex systems through partial differential equation solutions of a multi-parametric function approximation. Common neural network techniques of pattern classification or function approximation problems in general are based on whole-pattern similarity relationships of trained and tested data samples. They apply input variables of only absolute interval values, which may cause problems by far various training and testing data ranges. Differential polynomial neural network is a new type of neural network developed by the author, which constructs and substitutes an unknown general sum partial differential equation, defining a system model of dependent variables. It generates a total sum of fractional polynomialterms defining partial relative derivative dependent changes of some combinations of input variables. This type of regression is based only on trained generalized data relations. The character of relative data allows processing a wider r

Název v anglickém jazyce

Forecast Models of Partial Differential Equations using Polynomial Networks

Popis výsledku anglicky

Unknown data relations can describe lots of complex systems through partial differential equation solutions of a multi-parametric function approximation. Common neural network techniques of pattern classification or function approximation problems in general are based on whole-pattern similarity relationships of trained and tested data samples. They apply input variables of only absolute interval values, which may cause problems by far various training and testing data ranges. Differential polynomial neural network is a new type of neural network developed by the author, which constructs and substitutes an unknown general sum partial differential equation, defining a system model of dependent variables. It generates a total sum of fractional polynomialterms defining partial relative derivative dependent changes of some combinations of input variables. This type of regression is based only on trained generalized data relations. The character of relative data allows processing a wider r

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Příležitost pro mladé výzkumníky</a><br>

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 statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 238

ISBN

978-3-319-01795-2

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

1-11

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Praha

Datum konání akce

25. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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