Asynchronous Synthesis of a Neural Network Applied on Head Load Prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F13%3A43869917" target="_blank" >RIV/70883521:28140/13:43869917 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-33227-2_24" target="_blank" >http://dx.doi.org/10.1007/978-3-642-33227-2_24</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-33227-2_24" target="_blank" >10.1007/978-3-642-33227-2_24</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Asynchronous Synthesis of a Neural Network Applied on Head Load Prediction

Popis výsledku v původním jazyce

This paper introduces innovative method of an artificial neural network (ANN) optimization (synthesis) by means of Analytic Programming (AP). New asynchronous implementation of Self-Organizing Migration Algorithm (SOMA), which provides effective increaseof AP computing potential, is introduced here for time as well as original strategy of communication between SOMA and AP that further contribute towards efficiency in search for optimal ANN solution. The whole ANN synthesis algorithm is applied on the real case of heating plant model identification. The heating plant is located in the town of Most, Czech Republic. 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 distribution 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.

Název v anglickém jazyce

Asynchronous Synthesis of a Neural Network Applied on Head Load Prediction

Popis výsledku anglicky

This paper introduces innovative method of an artificial neural network (ANN) optimization (synthesis) by means of Analytic Programming (AP). New asynchronous implementation of Self-Organizing Migration Algorithm (SOMA), which provides effective increaseof AP computing potential, is introduced here for time as well as original strategy of communication between SOMA and AP that further contribute towards efficiency in search for optimal ANN solution. The whole ANN synthesis algorithm is applied on the real case of heating plant model identification. The heating plant is located in the town of Most, Czech Republic. 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 distribution 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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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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

000313767300024