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Mathematical Problems in Engineering
Volume 2011, Article ID 150294, 14 pages
Research Article

Mixture of Generalized Gamma Density-Based Score Function for Fastica

Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Received 14 September 2010; Accepted 21 September 2010

Academic Editor: Ezzat G. Bakhoum

Copyright © 2011 M. EL-Sayed Waheed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an entirely novel family of score functions for blind signal separation (BSS), based on the family of mixture generalized gamma density which includes generalized gamma, Weilbull, gamma, and Laplace and Gaussian probability density functions. To blindly extract the independent source signals, we resort to the FastICA approach, whilst to adaptively estimate the parameters of such score functions, we use Nelder-Mead for optimizing the maximum likelihood (ML) objective function without relaying on any derivative information. Our experimental results with source employing a wide range of statistics distribution show that Nelder-Mead technique produce a good estimation for the parameters of score functions.