Title :
Analytical development of the MMAXNLMS algorithm
Author :
Haddad, M.I. ; Mayyas, K.A. ; Khasawneh, M.A.
Author_Institution :
Dept. of Electr. Eng., Jordan Univ. of Sci. & Technol., Irbid, Jordan
Abstract :
In this paper an adaptive algorithm with reduced complexity is analysed for the white Gaussian input case. The new analysis is extended for the proposed case where updating includes more than one component of the weight vector. The new algorithm, which updates the weights corresponding to the element sizes of the data vector with the largest magnitude, is compared with the case where the updated weights are chosen randomly according to a uniform density function. Analysis is performed for both cases and the results are verified via computer simulations
Keywords :
Gaussian noise; adaptive signal processing; computational complexity; least mean squares methods; minimax techniques; white noise; MMAXNLMS algorithm; adaptive algorithm; analytical development; computer simulation; data vector; uniform density function; weight vector; white Gaussian noise; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Analytical models; Computational complexity; Computational modeling; Computer simulation; Density functional theory; Performance analysis; Random processes;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5041-3
DOI :
10.1109/ICASSP.1999.758283
