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Asynchronous Synthesis of a Neural Network Applied on Head Load Prediction

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dc.title Asynchronous Synthesis of a Neural Network Applied on Head Load Prediction en
dc.contributor.author Vařacha, Pavel
dc.relation.ispartof Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 2194-5365 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-642-33226-5
dc.identifier.isbn 978-3-642-33227-2
dc.date.issued 2013
utb.relation.volume 192
dc.citation.spage 225
dc.citation.epage 240
dc.event.title Nostradamus Conference
dc.event.location Ostrava
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2012-09
dc.event.edate 2012-09
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer-Verlag Berlin en
dc.identifier.doi 10.1007/978-3-642-33227-2_24
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-642-33227-2_24
dc.description.abstract 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 increase of 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. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1003145
utb.identifier.obdid 43870126
utb.identifier.scopus 2-s2.0-84874594661
utb.identifier.wok 000313767300024
utb.source d-wok
dc.date.accessioned 2013-02-24T06:52:29Z
dc.date.available 2013-02-24T06:52:29Z
utb.contributor.internalauthor Vařacha, Pavel
utb.fulltext.affiliation P. Vařacha Tomas Bata University in Zlín, Faculty of Applied Informatics, Nad Stráněmi 4511, Zlín, 760 05, Czech Republic varacha@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported in part by European Regional Development Fund within the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089.
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