A Family of Normalized Lms Algorithms

IEEE Signal Processing Letters

Volumn 1, Issue 3, 1994, Pages 49-51

  Douglas, S C ^a  

^a UNIVERSITY OF UTAH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; STATISTICAL METHODS; VECTORS;

LEAST MEAN SQUARE ALGORITHMS;

SIGNAL PROCESSING;

EID: 0028383996     PISSN: 10709908     EISSN: 15582361     Source Type: Journal    
DOI: 10.1109/97.295321     Document Type: Article

References (6)

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2. J. Nagumo and A. Noda, “A learning method for system identification,” IEEE Trans. Automat. Contr., vol. AC-12, no. 3, pp. 282–287, June 1967.
3. M. Tarrab and A. Feuer, “Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data,” IEEE Trans. Inform. Theory, vol. 34, no. 4, pp. 680–691, July 1988.
4. S. C. Douglas and T. H.-Y. Meng, “Normalized data nonlinearities for LMS adaptation,” IEEE Trans. Acoust. Speech Signal Processing, vol. 42, no. 6, June 1994.
5. I. Pitas, “Fast algorithms for running ordering and max/min calculation,” IEEE Trans. Circuits Syst., vol. 36, no. 6, pp. 795–804, June 1989.
6. S. H. Cho, Y. S. Kim, and J. A. Cadzow, “Adaptive FIR filtering based on minimum L⋈-norm,” in Proc. IEEE Pacific Rim Conf. Commun. Comput. Signal Processing (Victoria, Canada), May 1991, pp. 643–646, vol. 2.