On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics
Andrzej Cichocki , Ruck Thawonmas
AbstractMost of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.
|Journal series||Neural Processing Letters, ISSN 1370-4621, 1573-773X|
|Keywords in English||adaptive learning algorithms, Artificial Intelligence (incl. Robotics), blind signal processing, Electronic and Computer Engineering, neural networks, Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks, Operation Research/Decision Theory|
|Citation count*||66 (2014-02-08)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.