A Family of Minimal Residual Based Algorithm for Adaptive Filtering
The Minimal Residual (MR) is modified for adaptive
filtering application. Three forms of MR based algorithm are
presented: i) the low complexity SPCG, ii) MREDSI, and iii)
MREDSII. The low complexity is a reduced complexity version of a
previously proposed SPCG algorithm. Approximations introduced
reduce the algorithm to an LMS type algorithm, but, maintain the
superior convergence of the SPCG algorithm. Both MREDSI and
MREDSII are MR based methods with Euclidean direction of search.
The choice of Euclidean directions is shown via simulation to give
better misadjustment compared to their gradient search counterparts.
[1] G. K. Boray and M. D. Srinath, "Conjugate gradient techniques for
adaptive filtering", IEEE Trans. Circuits Syst., vol. 39, Jan. 1992, pp. 1-
10.
[2] P. S. Chang and A. N. Wilson, Jr., "Analysis of conjugate gradient
method for adaptive filtering", IEEE Trans. Signal Processing, vol. 48,
no. 2, Feb. 2000.
[3] N. A. Ahmad, "A globally convergent stochastic pairwise conjugate
gradient based adaptive filtering algorithm", IEEE Signal Process. Lett.,
vol. 15, Dec. 2008, pp. 914-917.
[4] Y. Saad, Iterative Methods for Sparse Linear Systems. Boston, MA:
PWS-Kent, 1996.
[5] S. Haykin, Adaptive Filter Theory, Englewood Cliffs, NJ: Prentice-Hall,
1991.
[6] N. A. Ahmad, ÔÇÿLow Complexity Stochastic Pairwise Conjugate Gradient
Based Adaptive Filtering Algorithm-, (to be published).
[7] G. F. Xu, T. Bose and J. Schroeder, "The Euclidean direction search
algorithm for adaptive filtering", Proceedings of the 1999 IEEE Int.
Symp. on Circuits and Systems, 3:146 - 149, 1999.
[1] G. K. Boray and M. D. Srinath, "Conjugate gradient techniques for
adaptive filtering", IEEE Trans. Circuits Syst., vol. 39, Jan. 1992, pp. 1-
10.
[2] P. S. Chang and A. N. Wilson, Jr., "Analysis of conjugate gradient
method for adaptive filtering", IEEE Trans. Signal Processing, vol. 48,
no. 2, Feb. 2000.
[3] N. A. Ahmad, "A globally convergent stochastic pairwise conjugate
gradient based adaptive filtering algorithm", IEEE Signal Process. Lett.,
vol. 15, Dec. 2008, pp. 914-917.
[4] Y. Saad, Iterative Methods for Sparse Linear Systems. Boston, MA:
PWS-Kent, 1996.
[5] S. Haykin, Adaptive Filter Theory, Englewood Cliffs, NJ: Prentice-Hall,
1991.
[6] N. A. Ahmad, ÔÇÿLow Complexity Stochastic Pairwise Conjugate Gradient
Based Adaptive Filtering Algorithm-, (to be published).
[7] G. F. Xu, T. Bose and J. Schroeder, "The Euclidean direction search
algorithm for adaptive filtering", Proceedings of the 1999 IEEE Int.
Symp. on Circuits and Systems, 3:146 - 149, 1999.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54889", author = "Noor Atinah Ahmad", title = "A Family of Minimal Residual Based Algorithm for Adaptive Filtering", abstract = "The Minimal Residual (MR) is modified for adaptive
filtering application. Three forms of MR based algorithm are
presented: i) the low complexity SPCG, ii) MREDSI, and iii)
MREDSII. The low complexity is a reduced complexity version of a
previously proposed SPCG algorithm. Approximations introduced
reduce the algorithm to an LMS type algorithm, but, maintain the
superior convergence of the SPCG algorithm. Both MREDSI and
MREDSII are MR based methods with Euclidean direction of search.
The choice of Euclidean directions is shown via simulation to give
better misadjustment compared to their gradient search counterparts.", keywords = "Adaptive filtering, Adaptive least square, Minimalresidual method.", volume = "4", number = "2", pages = "266-5", }