A Linear Feedforward Neural Network with Lateral Feedback Connections for Blind Source Separation

  • Authors:

  • Seungjin Choi

  • Affiliations:

  • -

  • Venue:
  • SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
  • Year:
  • 1997

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Abstract

We presents a new necessary and sufficient condition for the blind separation of sources having non-zero kurtosis, from their linear mixtures. It is shown here that a new blind separation criterion based on both odd (f(y)=y^3) and even (f(y)=y^2) functions, presents desirable solutions, provided that all source signals have negative kurtosis (sub-Gaussian) or have positive kurtosis (super-Gaussian). Based on this new separation criterion, a linear feedforward network with lateral feedback connections is constructed. Both theoretical and computer simulation results are presented.