Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering

Hatun M., Kocal O. H.

SIGNAL IMAGE AND VIDEO PROCESSING, vol.11, no.1, pp.137-144, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 1
  • Publication Date: 2017
  • Doi Number: 10.1007/s11760-016-0912-7
  • 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


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.