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dc.contributor.authorKoštial, Pavol
dc.contributor.authorJančíková, Zora
dc.contributor.authorBakošová, Daniela
dc.contributor.authorValíček, Jan
dc.contributor.authorHarničárová, Marta
dc.contributor.authorŠpička, Ivo
dc.date.accessioned2013-12-02T12:13:53Z
dc.date.available2013-12-02T12:13:53Z
dc.date.issued2013
dc.identifier.citationMeasurement Science Review. 2013, vol. 13, issue 5, p. 273-278.cs
dc.identifier.issn1335-8871
dc.identifier.urihttp://hdl.handle.net/10084/101319
dc.description.abstractThe paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.cs
dc.format.extent957290 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoencs
dc.publisherVersita - De Gruytercs
dc.relation.ispartofseriesMeasurement Science Reviewcs
dc.relation.urihttp://dx.doi.org/10.2478/msr-2013-0040cs
dc.rightsCopyright © 2011–2013 by Walter de Gruyter GmbHcs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cs
dc.subjectESPIcs
dc.subjectmodal analysiscs
dc.subjectneural networkscs
dc.subjecttirescs
dc.titleArtificial neural networks application in modal analysis of tirescs
dc.typearticlecs
dc.identifier.doi10.2478/msr-2013-0040
dc.rights.accessopenAccess
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume13cs
dc.description.issue5cs
dc.description.lastpage278cs
dc.description.firstpage273cs
dc.identifier.wos000326683300007


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Copyright © 2011–2013 by Walter de Gruyter GmbH
Except where otherwise noted, this item's license is described as Copyright © 2011–2013 by Walter de Gruyter GmbH