A new sparse convex combination of ZA-LLMS and RZA-LLMS algorithms [ZA-LLMS ve RZA-LLMS Algoritmalarinin Seyrek Sistemlerdeki Dişbükey Birleşimi]
Küçük Resim Yok
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- 113052
Anahtar Kelimeler
Kaynak
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
