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

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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.

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Authors and Affiliations

  1. Electrical-Electronic Engineering Department, Faculty of Engineering, Uludağ University, Bursa, Turkey

    Metin Hatun

  2. Computer Engineering Department, Faculty of Engineering, Yalova University, Yalova, Turkey

    Osman Hilmi Koçal

Authors
  1. Metin Hatun
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  2. Osman Hilmi Koçal
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Correspondence to Metin Hatun.

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Hatun, M., Koçal, O.H. Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering. SIViP 11, 137–144 (2017). https://doi.org/10.1007/s11760-016-0912-7

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  • DOI: https://doi.org/10.1007/s11760-016-0912-7

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