scholarly journals Independent Component Analysis Based on Neural Networks Using Hybrid Fixed-Point Algorithm

2002 ◽  
Vol 9B (5) ◽  
pp. 643-652
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fixed Point Algorithm

A new fixed-point algorithm for independent component analysis

2004 ◽  
Vol 56 ◽  
pp. 467-473 ◽  
Author(s):  
Zhenwei Shi ◽  
Huanwen Tang ◽  
Yiyuan Tang
Keyword(s):  
Fixed Point ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fixed Point Algorithm

A FAST FIXED-POINT ALGORITHM FOR INDEPENDENT COMPONENT ANALYSIS OF COMPLEX VALUED SIGNALS

2000 ◽  
Vol 10 (01) ◽  
pp. 1-8 ◽  
Author(s):  
ELLA BINGHAM ◽  
AAPO HYVÄRINEN
Keyword(s):  
Fixed Point ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fixed Point Algorithm ◽  
Local Consistency ◽  
Type Algorithm ◽  
Source Signals ◽  
Complex Valued ◽  
Point Type

Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. Also, the local consistency of the estimator given by the algorithm is proved.

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