Influence of hidden neuron number on the performance of ANN models applied to deinking flotation data

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dc.citation.epage232
dc.citation.rankM33
dc.citation.spage229
dc.contributor.authorBalanović, Katarina
dc.contributor.authorTrumić, Maja
dc.contributor.authorGavrilović, Tamara
dc.date.accessioned2025-12-12T12:20:30Z
dc.date.available2025-12-12T12:20:30Z
dc.date.issued2025
dc.description.abstractIn this paper, the influence of the optimal number of neurons in an artificial neural network model, applied to data obtained from deinking flotation is presented and analyzed. The results indicate that when a wellperforming model is selected, it is not necessary to search for the optimal number of hidden neurons. However, when the applied model does not yield satisfactory results, determining the optimal number of neurons becomes essential.
dc.identifier.doi10.5937/IOC25229B
dc.identifier.isbn978-86-6305-164-5
dc.identifier.urihttps://ioc.tfbor.bg.ac.rs/public/2025/Proceedings_IOC_2025.pdf
dc.identifier.urihttps://repozitorijum.tfbor.bg.ac.rs/handle/123456789/6053
dc.language.isoen
dc.publisherUniversity of Belgrade - Technical Faculty in Bor
dc.rights.licenseCC-BY-NC-ND
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceProceedings [Elektronski izvor] - 56th International October Conference on Mining and Metallurgy - IOC 2025, 22-25 October, 2025, Bor Lake, Serbia
dc.subjectArtificial neural networks
dc.subjectHidden neurons
dc.subjectFlotation
dc.subjectDeinking
dc.titleInfluence of hidden neuron number on the performance of ANN models applied to deinking flotation data
dc.typeconferenceObject
dc.type.versionpublishedVersion

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