Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering
SIGNAL IMAGE AND VIDEO PROCESSING, vol.11, no.1, pp.137-144, 2017 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 11 Issue: 1
- Publication Date: 2017
- Doi Number: 10.1007/s11760-016-0912-7
- Journal Name: SIGNAL IMAGE AND VIDEO PROCESSING
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.137-144
- Keywords: Adaptive filters, Successive over-relaxation, Gauss-Seidel, System identification, Convergence analysis
- Bursa Uludag University Affiliated: Yes
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.