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ANN synthesis for an agglomeration heating power consumption approximation

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dc.title ANN synthesis for an agglomeration heating power consumption approximation en
dc.contributor.author Vařacha, Pavel
dc.contributor.author Jašek, Roman
dc.relation.ispartof Recent Researches in Automatic Control - 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.identifier.isbn 978-1-61804-004-6
dc.date.issued 2011
dc.citation.spage 239
dc.citation.epage 244
dc.event.title 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.event.location Lanzarote, Canary Islands
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2011-05-27
dc.event.edate 2011-05-29
dc.type conferenceObject
dc.language.iso en
dc.relation.uri http://www.wseas.us/e-library/conferences/2011/Lanzarote/ACMOS/ACMOS-45.pdf
dc.subject Analytic Programming en
dc.subject Approximation en
dc.subject Heating plant en
dc.subject Neural Network en
dc.subject Optimization en
dc.subject SOMA en
dc.description.abstract This article deals with Analytic Programming (AP) which was proven to be highly effective tool of Artificial Neural Network (ANN) synthesis and optimization. AP is successfully applied here to evolve such ANN which is able to approximate heating power consumption of an agglomeration, in dependence on time and atmospherics temperature, more accurately than standard methods. An experiment described in the paper was realized on real life data from Most agglomeration and heating plant situated in Komořany, Czech Republic. Resulted ANN brings 19% increase of approximation accuracy. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004800
utb.identifier.obdid 43867492
utb.identifier.scopus 2-s2.0-82555198162
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:55:32Z
dc.date.available 2015-06-04T12:55:32Z
utb.contributor.internalauthor Vařacha, Pavel
utb.contributor.internalauthor Jašek, Roman
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