Rodyti trumpą aprašą

dc.contributor.authorGerasimov, Vadim
dc.contributor.authorJonaitis, Gintaras
dc.contributor.authorJonkus, Vytautas
dc.date.accessioned2023-09-18T17:08:14Z
dc.date.available2023-09-18T17:08:14Z
dc.date.issued2015
dc.identifier.issn2029-1035
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/120072
dc.description.abstractGeneral principles for creating the electronic system devoted for processing and recognition of myoelectric signals were observed. Due to functional importance, usage of Artificial Neural Networks (ANN) for recognicion purposes were estimated as principleeng
dc.format.extentp. 7-12
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.source.urihttp://journal.kolegija.lt/iitsbe/2015/Gerasimov-application1-IITSBE-2015-1(18)-7-12.pdf
dc.titleApplication of artificial neural networks for human muscle signal analysis and mechanical equipment control. 1. Problem overview.
dc.typeStraipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed source
dcterms.references5
dc.type.pubtypeS4 - Straipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed publication
dc.contributor.institutionVilniaus universitetas
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyMechanikos fakultetas / Faculty of Mechanics
dc.contributor.departmentBiomechanikos katedra / Department of Biomechanics
dc.subject.researchfieldT 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering
dc.subject.researchfieldN 002 - Fizika / Physics
dc.subject.enArtificial Neural Networks
dc.subject.enANN
dc.subject.enrecognition of myoelectric signals
dc.subject.enelectromyogram
dc.subject.enlinear regression method
dc.subject.ensupervised learning
dc.subject.enlogistic regression
dc.subject.enartificial neuron.
dcterms.sourcetitleInnovative infotechnologies for science, business and education
dc.description.volumeVol. 1 (18)
dc.publisher.nameVilniaus verslo kolegija
dc.publisher.cityVilnius
dc.identifier.elaba26997217


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