Title :
A comparison of BSS algorithms
Author :
Singh, Yogesh ; Rai, C.S.
Author_Institution :
Sch. of Inf. Technol., G.G.S. Indraprastha Unv., Delhi, India
Abstract :
Several gradient-based algorithms exist for performing blind source separation (BSS). In this paper we compare three most popular neural algorithms: EASI, natural gradient and Bell-Sejnowski algorithms. The effectiveness of these algorithms depends upon the nonlinear activation function. These algorithms were evaluated with different nonlinear functions for sub-Gaussian and super-Gaussian sources
Keywords :
neural nets; nonlinear functions; signal detection; Bell-Sejnowski algorithm; EASI; adaptive source separation; blind source separation; natural gradient algorithm; neural nets; Additive noise; Blind source separation; Convergence; Entropy; Information technology; Iterative algorithms; Mutual information; Probability density function; Source separation; Vectors;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939484
