Complex System Modeling with General Differential Equations solved by means of Polynomial Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86095954" target="_blank" >RIV/61989100:27240/15:86095954 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86095954

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-27644-1_7" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-27644-1_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-27644-1_7" target="_blank" >10.1007/978-3-319-27644-1_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Complex System Modeling with General Differential Equations solved by means of Polynomial Networks

Popis výsledku v původním jazyce

Differential equations can describe physical and natural systems, which be-havior only explicit exact functions are not able to model. Complex dynamic systems are characterized by a high variability of time-fluctuating data rela-tions of a great number of state variables. Systems of differential equations can describe them but they are too unstable to be modeled unambiguously by means of standard soft computing techniques. In some cases the correct form of a differential equation might absent or it is difficult to express. Dif-ferential polynomial neural network is a new neural network type, which forms and solves an unknown general partial differential equation of an ap-proximation of a searched function, described by discrete data observations. It generates convergent sum series of relative partial polynomial derivative terms, which can substitute for a partial or/and ordinary differential equation solution. This type of non-linear regression decomposes a system model, de-scribed by

Název v anglickém jazyce

Complex System Modeling with General Differential Equations solved by means of Polynomial Networks

Popis výsledku anglicky

Differential equations can describe physical and natural systems, which be-havior only explicit exact functions are not able to model. Complex dynamic systems are characterized by a high variability of time-fluctuating data rela-tions of a great number of state variables. Systems of differential equations can describe them but they are too unstable to be modeled unambiguously by means of standard soft computing techniques. In some cases the correct form of a differential equation might absent or it is difficult to express. Dif-ferential polynomial neural network is a new neural network type, which forms and solves an unknown general partial differential equation of an ap-proximation of a searched function, described by discrete data observations. It generates convergent sum series of relative partial polynomial derivative terms, which can substitute for a partial or/and ordinary differential equation solution. This type of non-linear regression decomposes a system model, de-scribed by

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

Advances in Intelligent Systems and Computing. Volume 423

ISBN

978-3-319-27642-7

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

10

Strana od-do

63-67

Název nakladatele

Springer

Místo vydání

Basel

Místo konání akce

Issyk Kul

Datum konání akce

25. 8. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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