Constructing Ordinary Sum Differential Equations using Polynomial Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86090323" target="_blank" >RIV/61989100:27240/14:86090323 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86090323

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0020025514005969" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0020025514005969</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ins.2014.05.036" target="_blank" >10.1016/j.ins.2014.05.036</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Constructing Ordinary Sum Differential Equations using Polynomial Networks

Popis výsledku v původním jazyce

Data relations can define general sum partial differential equations of a composite function additive derivative model. Time-series data observations can analogously describe an ordinary sum differential equation with time derivatives, which is possibleto be solved using partial derivative term substitutions of time-dependent series. Differential polynomial neural network is a new type of neural network, which constructs and substitutes for an unknown general partial differential equation from data observations, developed by the author. It generates sum series of convergent partial polynomial derivative terms, which can describe an unknown complex function time-series. This type of non-linear regression decomposes a system model, described by the general differential equation, into many partial low order derivative specifications of selected relative sum terms. Common soft-computing techniques in general can apply input variables of only absolute interval values of a specific data ran

Název v anglickém jazyce

Constructing Ordinary Sum Differential Equations using Polynomial Networks

Popis výsledku anglicky

Data relations can define general sum partial differential equations of a composite function additive derivative model. Time-series data observations can analogously describe an ordinary sum differential equation with time derivatives, which is possibleto be solved using partial derivative term substitutions of time-dependent series. Differential polynomial neural network is a new type of neural network, which constructs and substitutes for an unknown general partial differential equation from data observations, developed by the author. It generates sum series of convergent partial polynomial derivative terms, which can describe an unknown complex function time-series. This type of non-linear regression decomposes a system model, described by the general differential equation, into many partial low order derivative specifications of selected relative sum terms. Common soft-computing techniques in general can apply input variables of only absolute interval values of a specific data ran

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

Information sciences

ISSN

0020-0255

e-ISSN

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Svazek periodika

Volume 281

Číslo periodika v rámci svazku

Multimedia Modeling

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

"462-477"

Kód UT WoS článku

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EID výsledku v databázi Scopus

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