Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Hatun, Metin; Koçal, Osman
• Is Part Of: Signal, image and video processing. Volume 11:Issue 1(2017)
• Page Start: 137
• Page End: 144
• Publication Date: 2017-01
• Subjects: Adaptive filters -- Successive over-relaxation -- Gauss–Seidel -- System identification -- Convergence analysis; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0912-7
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract 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.