Composing and Solving General Differential Equations using Extended Polynomial Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86095956" target="_blank" >RIV/61989100:27240/15:86095956 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86095956

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7312058" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7312058</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INCoS.2015.28" target="_blank" >10.1109/INCoS.2015.28</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Composing and Solving General Differential Equations using Extended Polynomial Networks

Popis výsledku v původním jazyce

Multi-variable data relations can define a partial differential equation, which describes an unknown complex function on a basis of discrete observations, using the similarity model analysis methods. Time-series can form an ordinary differential equation, which is analogously possible to replace by partial derivatives of the same type time-dependent observations. Polynomial neural networks can compose and solve an unknown general partial differential equation of a searched function or pattern model by means of low order composite multi-variable derivative fractions. Convergent sum series of relative terms, produced by polynomial networks, describe partial dependent derivative changes of some polynomial combinations of input variables and can substitutefor the general differential equation. This non-linear regression type is based on learned generalized partial elementary data relations, decomposed into a polynomial network derivative structure, which is able to define and create more

Název v anglickém jazyce

Composing and Solving General Differential Equations using Extended Polynomial Networks

Popis výsledku anglicky

Multi-variable data relations can define a partial differential equation, which describes an unknown complex function on a basis of discrete observations, using the similarity model analysis methods. Time-series can form an ordinary differential equation, which is analogously possible to replace by partial derivatives of the same type time-dependent observations. Polynomial neural networks can compose and solve an unknown general partial differential equation of a searched function or pattern model by means of low order composite multi-variable derivative fractions. Convergent sum series of relative terms, produced by polynomial networks, describe partial dependent derivative changes of some polynomial combinations of input variables and can substitutefor the general differential equation. This non-linear regression type is based on learned generalized partial elementary data relations, decomposed into a polynomial network derivative structure, which is able to define and create more

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Intelligent Networking and Collaborative Systems INCoS-2015 : 7th International Conference : proceedings : September 2-4, 2015, Taipei, Tchaj-wan

ISBN

978-1-4673-7694-5

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

110 - 115

Název nakladatele

IEEE

Místo vydání

Danvers

Místo konání akce

Taipei

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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