Publication:
Transient analysis of convexly constrained mixture methods

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dc.contributor.coauthorN/A
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentN/A
dc.contributor.departmentN/A
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.kuauthorKozat, Süleyman Serdar
dc.contributor.kuauthorDönmez, Mehmet Ali
dc.contributor.kuauthorÖzkan, Hüseyin
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofileMaster Student
dc.contributor.kuprofilePhD Student
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.yokid177972
dc.contributor.yokidN/A
dc.contributor.yokidN/A
dc.date.accessioned2024-11-10T00:04:36Z
dc.date.issued2012
dc.description.abstractWe study the transient performances of three convexly constrained adaptive combination methods that combine outputs of two adaptive filters running in parallel to model a desired unknown system. We propose a theoretical model for the mean and mean-square convergence behaviors of each algorithm. Specifically, we provide expressions for the time evolution of the mean and the variance of the combination parameters, as well as for the mean square errors. The accuracy of the theoretical models are illustrated through simulations in the case of a mixture of two LMS filters with different step sizes.
dc.description.indexedbyScopus
dc.description.indexedbyWoS
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsorshipIEEE Signal Processing Society
dc.description.sponsorshipIBM Faculty Award and Outstanding Young Scientist Award Program, Turkish Academy of Sciences.
dc.identifier.doi10.1109/MLSP.2012.6349801
dc.identifier.isbn9781-4673-1026-0
dc.identifier.issn2161-0363
dc.identifier.linkhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84870670947anddoi=10.1109%2fMLSP.2012.6349801andpartnerID=40andmd5=cf0ce3828061593fc7ca5dedc96e8357
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-84870670947
dc.identifier.urihttp://dx.doi.org/10.1109/MLSP.2012.6349801
dc.identifier.urihttps://hdl.handle.net/20.500.14288/16294
dc.identifier.wos311966000092
dc.keywordsConvergence behaviors
dc.keywordsConvex combinations
dc.keywordsMean-square
dc.keywordsMixture method
dc.keywordsRunning-in
dc.keywordsStep size
dc.keywordsTheoretical models
dc.keywordsTime evolutions
dc.keywordsTransient performance
dc.keywordsAdaptive filtering
dc.keywordsAdaptive filters
dc.keywordsComputer simulation
dc.keywordsLearning systems
dc.keywordsMean square error
dc.keywordsSignal processing
dc.keywordsMixtures
dc.languageEnglish
dc.publisherIEEE
dc.sourceIEEE International Workshop on Machine Learning for Signal Processing, MLSP
dc.subjectEngineering
dc.subjectElectrical and electronics engineering
dc.titleTransient analysis of convexly constrained mixture methods
dc.typeConference proceeding
dspace.entity.typePublication
local.contributor.authorid0000-0002-6488-3848
local.contributor.authoridN/A
local.contributor.authorid0000-0002-5539-9085
local.contributor.kuauthorKozat, Süleyman Serdar
local.contributor.kuauthorDönmez, Mehmet Ali
local.contributor.kuauthorÖzkan, Hüseyin
relation.isOrgUnitOfPublication21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isOrgUnitOfPublication.latestForDiscovery21598063-a7c5-420d-91ba-0cc9b2db0ea0

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