Approximation of Multi-parametric Functions Using The Differential Polynomial Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86087417" target="_blank" >RIV/61989100:27740/13:86087417 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.iaumath.com/content/7/1/33" target="_blank" >http://www.iaumath.com/content/7/1/33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/2251-7456-7-33" target="_blank" >10.1186/2251-7456-7-33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Approximation of Multi-parametric Functions Using The Differential Polynomial Neural Network

Popis výsledku v původním jazyce

Unknown data relations can describe a lot of complex systems through a partial differential equation solution of a multi-parametric function approximation. Common artificial neural network techniques of a pattern classification or function approximationin general are based on whole-pattern similarity relations of trained and tested data samples. It applies input variables of only absolute interval values, which may cause problems by far various training and testing data ranges. Differential polynomialneural network is a new type of neural network developed by the author, which constructs and resolves an unknown general partial differential equation, describing a system model of dependent variables. It creates a sum of fractional polynomial terms, defining partial mutual derivative changes of input variables combinations. This type of regression is based on learned generalized data relations. It might improve dynamic system models a standard time-series prediction, as the character of

Název v anglickém jazyce

Approximation of Multi-parametric Functions Using The Differential Polynomial Neural Network

Popis výsledku anglicky

Unknown data relations can describe a lot of complex systems through a partial differential equation solution of a multi-parametric function approximation. Common artificial neural network techniques of a pattern classification or function approximationin general are based on whole-pattern similarity relations of trained and tested data samples. It applies input variables of only absolute interval values, which may cause problems by far various training and testing data ranges. Differential polynomialneural network is a new type of neural network developed by the author, which constructs and resolves an unknown general partial differential equation, describing a system model of dependent variables. It creates a sum of fractional polynomial terms, defining partial mutual derivative changes of input variables combinations. This type of regression is based on learned generalized data relations. It might improve dynamic system models a standard time-series prediction, as the character of

Druh

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

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 periodika

Mathematical Sciences

ISSN

2251-7456

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

7

Strana od-do

1-7

Kód UT WoS článku

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EID výsledku v databázi Scopus

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