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

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.