Koçal, Osman Hilmi2023-02-032023-02-032016-05-17Hatun, 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), 137-144.1863-1703https://doi.org/10.1007/s11760-016-0912-71863-1711https://link.springer.com/article/10.1007/s11760-016-0912-7http://hdl.handle.net/11452/30829A 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.eninfo:eu-repo/semantics/closedAccessEngineeringImaging science & photographic technologyAdaptive filtersSuccessive over-relaxationGauss-seidelSystem identificationConvergence analysisAdaptive algorithmsAdaptive filteringAlgorithmsIdentification (control systems)Mean square errorReligious buildingsStochastic systemsConvergence analysisConvergence ratesEnsemble-averagedGradient based algorithmParameter vectorsRecursive least square (RLS)RLS algorithmsRLS algorithmsSuccessive over relaxationAdaptive filtersArticle0003922888000182-s2.0-84976313525137144111Engineering, electrical & electronicImaging science & photographic technologyRecursive Algorithm; Adaptive Filtering; Beamforming