A Cascade Neural Network for Blind Signal Extraction without Spurious Equilibria
Ruck Thawonmas , Andrzej Cichocki , Shun-ichi Amari
AbstractWe present a cascade neural network for blind source extraction. We propose a family of unconstrained optimization criteria, from which we derive a learning rule that can extract a single source signal from a linear mixture of source signals. To prevent the newly extracted source signal from being extracted again in the next processing unit, we propose another unconstrained optimization criterion that uses knowledge of this signal. From this criterion, we then derive a learning rule that deflates from the mixture the newly extracted signal. By virtue of blind extraction and deflation processing, the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources, with the number of sources not known in advance. We prove analytically that the proposed criteria both for blind extraction and deflation processing have no spurious equilibria. In addition, the proposed criteria do not require whitening of mixed signals. We also demonstrate the validity and performance of the presented neural network by computer simulation experiments.
|Journal series||IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, ISSN 0916-8508|
|Publication indicators||: 2013 = 0.226 (2) - 2013=0.229 (5)|
|Citation count*||36 (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.