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dc.contributor.authorStašionis, Liudas
dc.contributor.authorSerackis, Artūras
dc.date.accessioned2023-09-18T16:27:40Z
dc.date.available2023-09-18T16:27:40Z
dc.date.issued2015
dc.identifier.issn2255-8942
dc.identifier.other(BIS)VGT02-000031408
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/114175
dc.description.abstractThe paper presents a new method for adaptive selection of Self-Organizing Map (SOM) self-training endpoint. A method is based on the estimation of the newly introduced parameter init and the learning depth parameter. In order to propose an optimal range of values, the influence of the selected learning depth parameter to the performance of SOM was tested experimentally using input data with uniform distribution. Additionally, four endpoint selection approaches were tested in spectrum sensing application where the SOM based detector was used to detect primary user emissions in 25 MHz wide spectrum band. Three alternative SOM self-training endpoint selection methods were tested on the same topology based SOM. In comparison to SOM self-training endpoint selection algorithm, based on the cluster quality estimation, the proposed method required from 2 : 6 % (for the SOM with small number of neurons) to 44 : 6 % (for the SOM with higher number of neurons) less iterations to reach the endpoint and preserve the similar sensitivity of the spectrum sensor based on SOM.eng
dc.format.extentp. 294-306
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyEmerging Sources Citation Index (Web of Science)
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyVINITI
dc.source.urihttp://www.bjmc.lu.lv/fileadmin/user_upload/lu_portal/projekti/bjmc/Contents/3_4_6_Serackis.pdf
dc.subjectIK04 - Skaitmeninės signalų apdorojimo technologijos / Digital signal processing technologies
dc.titleA new method for adaptive selection of Self-Organizing Map self-training endpoint
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references23
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyElektronikos fakultetas / Faculty of Electronics
dc.subject.researchfieldT 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enSelf-Organizing Map
dc.subject.enSOM self-training
dc.subject.enAdaptive endpoint
dc.subject.enSpectrum sensor
dcterms.sourcetitleBaltic journal of modern computing (BJMC)
dc.description.issueno. 4
dc.description.volumeVol. 3
dc.publisher.nameUniversity of Latvia
dc.publisher.cityRyga
dc.identifier.elaba15949884


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