Bu öğeden alıntı yapmak, öğeye bağlanmak için bu tanımlayıcıyı kullanınız: http://hdl.handle.net/11452/30829
Başlık: Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering
Yazarlar: Koçal, Osman Hilmi
Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü.
0000-0003-0279-5508
Hatun, Metin
AAH-2199-2021
54684165800
Anahtar kelimeler: Engineering
Imaging science & photographic technology
Adaptive filters
Successive over-relaxation
Gauss-seidel
System identification
Convergence analysis
Adaptive algorithms
Adaptive filtering
Algorithms
Identification (control systems)
Mean square error
Religious buildings
Stochastic systems
Convergence analysis
Convergence rates
Ensemble-averaged
Gradient based algorithm
Parameter vectors
Recursive least square (RLS)
RLS algorithms
RLS algorithms
Successive over relaxation
Adaptive filters
Yayın Tarihi: 17-May-2016
Yayıncı: Springer
Atıf: Hatun, M. ve Koçal, O. H. (2017). ''Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering''. Signal, Image and Video Processing, 11(1), 137-144.
Özet: A stochastic convergence analysis of the parameter vector estimation obtained by the recursive successive over-relaxation (RSOR) algorithm is performed in mean sense and mean-square sense. Also, excess of mean-square error and misadjustment analysis of the RSOR algorithm is presented. These results are verified by ensemble-averaged computer simulations. Furthermore, the performance of the RSOR algorithm is examined using a system identification example and compared with other widely used adaptive algorithms. Computer simulations show that the RSOR algorithm has better convergence rate than the widely used gradient-based algorithms and gives comparable results obtained by the recursive least-squares RLS algorithm.
URI: https://doi.org/10.1007/s11760-016-0912-7
1863-1711
https://link.springer.com/article/10.1007/s11760-016-0912-7
http://hdl.handle.net/11452/30829
ISSN: 1863-1703
Koleksiyonlarda Görünür:Scopus
Web of Science

Bu öğenin dosyaları:
Bu öğeyle ilişkili dosya bulunmamaktadır.


DSpace'deki bütün öğeler, aksi belirtilmedikçe, tüm hakları saklı tutulmak şartıyla telif hakkı ile korunmaktadır.