Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering
No Thumbnail Available
Date
2016-05-17
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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.
Description
Keywords
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
Citation
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.