Synthesis of a Neural Network via Analytic Programming Methodology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F13%3A43869450" target="_blank" >RIV/70883521:28140/13:43869450 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Synthesis of a Neural Network via Analytic Programming Methodology

Popis výsledku v původním jazyce

This paper introduces innovative method of an artificial neural network (ANN) optimization (synthesis) by means of Analytic Programming (AP). The method proves itself to be especially effective when formally identified non-neural parts of the heating plant model need to be made more accurate. Asynchronous distribu-tion plays the key role here as the heating plant behavior data has to be acquired from a very large database and therefore learning of ANN may require a lot of computation time. The method was successfully tested on the real life problems as well as on widely recognized benchmark functions with respect to the function approximation, prediction and problems. The ANN synthesis software was de-signed based on .NET Framework technology. The resulting software is capable of automatic synthesis and optimizing the ANN based on the user-given data within a reasonable time. The ANN synthesis proves to be a useful and efficient tool for nonlinear modeling in comparison with competing

Název v anglickém jazyce

Synthesis of a Neural Network via Analytic Programming Methodology

Popis výsledku anglicky

This paper introduces innovative method of an artificial neural network (ANN) optimization (synthesis) by means of Analytic Programming (AP). The method proves itself to be especially effective when formally identified non-neural parts of the heating plant model need to be made more accurate. Asynchronous distribu-tion plays the key role here as the heating plant behavior data has to be acquired from a very large database and therefore learning of ANN may require a lot of computation time. The method was successfully tested on the real life problems as well as on widely recognized benchmark functions with respect to the function approximation, prediction and problems. The ANN synthesis software was de-signed based on .NET Framework technology. The resulting software is capable of automatic synthesis and optimizing the ANN based on the user-given data within a reasonable time. The ANN synthesis proves to be a useful and efficient tool for nonlinear modeling in comparison with competing

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/ED2.1.00%2F03.0089" target="_blank" >ED2.1.00/03.0089: Centrum bezpečnostních, informačních a pokročilých technologií (CEBIA-Tech)</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

Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems

ISBN

978-3-642-33226-5

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

16

Strana od-do

225-240

Název nakladatele

Springer-Verlag Berlin

Místo vydání

Heidelberg

Místo konání akce

Ostrava

Datum konání akce

5. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—