Title :
The ∈-normalized sign regressor least mean fourth (NSRLMF) adaptive algorithm
Author :
Faiz, Mohammed Mujahid Ulla ; Zerguine, Azzedine
Author_Institution :
Electr. Eng. Dept., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
Abstract :
In this paper, a new algorithm, the ϵ-normalized sign regressor least mean fourth (NSRLMF) algorithm is presented as a substitute for the ϵ-normalized least mean fourth (NLMF) algorithm. This new algorithm reduces significantly the computational load. Moreover, the proposed algorithm has similar convergence properties as those of the ϵ-NLMF algorithm. Finally, simulations corroborate very well the theoretical findings.
Keywords :
convergence of numerical methods; least mean squares methods; regression analysis; ϵ-normalized sign regressor least mean fourth algorithm; adaptive algorithm; computational load; convergence properties; Algorithm design and analysis; Approximation algorithms; Convergence; Signal processing; Signal processing algorithms; Steady-state; Vectors;
Conference_Titel :
Information Science, Signal Processing and their Applications (ISSPA), 2012 11th International Conference on
Conference_Location :
Montreal, QC
Print_ISBN :
978-1-4673-0381-1
Electronic_ISBN :
978-1-4673-0380-4
DOI :
10.1109/ISSPA.2012.6310571
