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dc.contributor.authorBukovský , Ivo
dc.contributor.authorKolovratník , Michal
dc.date.accessioned2017-02-08T11:54:16Z
dc.date.available2017-02-08T11:54:16Z
dc.date.issued2012
dc.identifier.citationActa Polytechnica. 2012, vol. 52, no. 3.
dc.identifier.issn1210-2709 (print)
dc.identifier.issn1805-2363 (online)
dc.identifier.urihttp://hdl.handle.net/10467/66932
dc.description.abstractThis paper presents a non-conventional dynamic neural network that was designed for real time prediction of NOx at the coal powder power plant Mělnik 1, and results on real data are shown and discussed. The paper also presents the signal preprocessing techniques, the input-reconfigurable architecture, and the learning algorithm of the proposed neural network, which was designed to handle the non-stationarity of the burning process as well as individual failures of the measured variables. The advantages of our designed neural network over conventional neural networks are discussed.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherČeské vysoké učení technické v Prazecs
dc.publisherCzech Technical University in Pragueen
dc.relation.ispartofseriesActa Polytechnica
dc.relation.urihttps://ojs.cvut.cz/ojs/index.php/ap/article/view/1538
dc.rightsCreative Commons Attribution 4.0 International Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectdynamic neural networksen
dc.subjectpredictionen
dc.subjectNOx emissionsen
dc.subjectsignal processingen
dc.titleA Neural Network Model for Predicting NOx at the Mělník 1
dc.typearticleen
dc.date.updated2017-02-08T11:54:16Z
dc.rights.accessopenAccess
dc.type.statusPeer-reviewed
dc.type.versionpublishedVersion


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Creative Commons Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International License