Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/9001
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dc.rights.licenseopenAccess-
dc.contributor.authorTomić, Jelena-
dc.contributor.authorBogojevic, Nebojsa-
dc.contributor.authorPljakić M.-
dc.contributor.authorŠumarac Pavlović D.-
dc.date.accessioned2020-09-19T17:13:05Z-
dc.date.available2020-09-19T17:13:05Z-
dc.date.issued2016-
dc.identifier.issn0001-4966-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/9001-
dc.description.abstract© 2016 Acoustical Society of America. Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.-
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.sourceJournal of the Acoustical Society of America-
dc.titleAssessment of traffic noise levels in urban areas using different soft computing techniques-
dc.typearticle-
dc.identifier.doi10.1121/1.4964786-
dc.identifier.scopus2-s2.0-84991627440-
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo

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