## Abstract The least mean squares (LMS) algorithm, the most commonly used channel estimation and equalization technique, converges very slowly. The convergence rate of the LMS algorithm is quite sensitive to the adjustment of the stepβsize parameter used in the update equation. Therefore, many stu
β¦ LIBER β¦
Fuzzy step-size adjustment for the LMS algorithm
β Scribed by Woon-Seng Gan
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 384 KB
- Volume
- 49
- Category
- Article
- ISSN
- 0165-1684
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A novel variable step size adjustment me
β
Ali Ozen
π
Article
π
2011
π
John Wiley and Sons
π
English
β 290 KB
A robust interference cancellation techn
β
Cheng, Chia-Hsin ;Wen, Jyh-Horng ;Chen, Yu-Fan ;Lin, Jen-Yung
π
Article
π
2008
π
John Wiley and Sons
π
English
β 397 KB
A simple variable step size LMS adaptive
β
Tarek I. Haweel
π
Article
π
2004
π
John Wiley and Sons
π
English
β 321 KB
Optimum design of the LMS algorithm usin
β
Chin-Liang Wang; Rong-Yih Chen
π
Article
π
1992
π
Elsevier Science
π
English
β 436 KB
Adaptive step adjustment for a stochasti
β
F. Mirzoakhmedov; S.P. Uryas'ev
π
Article
π
1983
π
Elsevier Science
β 462 KB
This matrix does not satisfy lladamard's test of non-degeneracy, nor does it satisfy Brauer's test, yet it satisfies the conditions of Theorem 17. ## Note. A matrix that satisfies Hadamard's non-degeneracy test will obviously satisfy the conditions of Theorem 17. Theorem 17 gives an additive ana
A simple step size selection algorithm f
β
L.F. Shampine; A. Witt
π
Article
π
1995
π
Elsevier Science
π
English
β 876 KB