A new sparse convex combination of ZA-LLMS and RZA-LLMS algorithms [ZA-LLMS ve RZA-LLMS Algoritmalarinin Seyrek Sistemlerdeki Dişbükey Birleşimi]

dc.contributor.authorSalman M.S.
dc.contributor.authorHameed A.A.
dc.contributor.authorTuran C.
dc.contributor.authorKarlik B.
dc.date.accessioned2020-03-26T19:08:00Z
dc.date.available2020-03-26T19:08:00Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- 113052en_US
dc.description.abstractIn the last decade, several algorithms have been proposed for performance improvement of adaptive filters in sparse system identification. In this paper, we propose a new convex combination of two different algorithms as zero-attracting leaky least-mean-square (ZA-LLMS) and reweighted zero-attracting leaky-least-mean square (RZA-LLMS) algorithms in sparse system identification setting. The performances of the aforementioned algorithms has been tested and compared to the result of the new combination. Simulations show that the proposed algorithm has a good ability to track the MSD curves of the other algorithms in additive white Gaussian noise (AWGN) and additive correlated Gaussian noise (ACGN) environments. © 2015 IEEE.en_US
dc.identifier.doi10.1109/SIU.2015.7129925en_US
dc.identifier.endpage714en_US
dc.identifier.isbn9.78147E+12
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage711en_US
dc.identifier.urihttps://dx.doi.org/10.1109/SIU.2015.7129925
dc.identifier.urihttps://hdl.handle.net/20.500.12395/32755
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleA new sparse convex combination of ZA-LLMS and RZA-LLMS algorithms [ZA-LLMS ve RZA-LLMS Algoritmalarinin Seyrek Sistemlerdeki Dişbükey Birleşimi]en_US
dc.typeConference Objecten_US

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