Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
G. C. Goodwin and R. L. Payne, Dynamic System Identification: Experiment Design and Data Analysis, Academic Press, NewYork, NY, 1977.
S. Haykin, Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, NJ, 4th edition, 2002.
S. H. Ardalan, ‘‘Floating-point analysis of recursive least-squares and least-mean squares adaptive filters,’’ IEEE Trans. on Circuits and Systems, vol. CAS-33, pp. 1192-1208, Dec. 1986.
J. M. Cioffi, ‘‘Limited precision effects in adaptive filtering,’’ IEEE Trans. on Circuits and Systems, vol. CAS-34, pp. 821-833, July 1987.
R. S. Medaugh and L. J. Griffiths, ‘‘A comparison of two linear predictors,’’ Proc. IEEE Intern. Conf. on Acoust., Speech, Signal Processing, Atlanta, GA, pp. 293-296, April 1981.
F. Ling and J. G. Proakis, ‘‘Nonstationary learning characteristics of least squares adaptive estimation algorithms,’’ Proc. IEEE Intern. Conf. on Acoust., Speech, Signal Processing, San Diego, CA, pp. 30.3.1.-30.3.4, March 1984.
E. Eleftheriou and D. D. Falconer, ‘‘Tracking properties and steady-state performance of RLS adaptive filter algorithms,’’ IEEE Trans. on Acoust., Speech, and Signal Processing, vol. ASSP- 34, pp. 1097-1110, Oct. 1986.
J. M. Cioffi and T. Kailath, ‘‘Fast recursive-least-squares transversal filters for adaptive filtering,’’ IEEE Trans. on Acoust., Speech, and Signal Processing, vol. ASSP-32, pp. 304-337, April 1984.
S.Ardalan, ‘‘On the sensitivity of transversal RLS algorithms to random perturbations in the filter coefficients,’’ IEEE Trans. on Acoust., Speech, and Signal Processing, vol. 36, pp. 1781-1783, Nov. 1988.
C. R. Johnson, Jr., Lectures on Adaptive Parameter Estimation, Prentice Hall, Englewood Cliffs, NJ, 1988.
O. M. Macchi and N. J. Bershad, ‘‘Adaptive recovery of a chirped sinusoid in noise, Part 1: performance of the RLS algorithm,’’ IEEE Trans. on Signal Processing, vol. 39, pp. 583-594, March 1991.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag US
About this chapter
Cite this chapter
Diniz, P.S. (2008). Conventional Rls Adaptive Filter. In: Adaptive Filtering. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-68606-6_5
Download citation
DOI: https://doi.org/10.1007/978-0-387-68606-6_5
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-31274-3
Online ISBN: 978-0-387-68606-6
eBook Packages: EngineeringEngineering (R0)