Stable higher-order recurrent neural network structures for nonlinear blind source separation

Authors:
Yannick Deville;Shahram Hosseini
Affiliations:
Université Paul Sabatier Toulouse 3-Observatoire Midi-Pyrénées-CNRS, Laboratoire d'Astrophysique de Toulouse-Tarbes, UMR, Toulouse, France;Université Paul Sabatier Toulouse 3-Observatoire Midi-Pyrénées-CNRS, Laboratoire d'Astrophysique de Toulouse-Tarbes, UMR, Toulouse, France
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

Citing 3
Cited 0

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Blind source separation for convolutive mixtures

Signal Processing
Self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: stability analysis and optimization of asymptotic behavior

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper concerns our general recurrent neural network structures for nonlinear blind source separation, especially suited to polynomial mixtures. We here focus on linear-quadratic mixtures. We introduce an extended structure, with additional free parameters as compared to the structure that we previously proposed. We derive the equilibrium points of our new structure, thus showing that it has no spurious fixed points. We analyze its stability in detail and propose a practical procedure for selecting its free parameters, so as to guarantee the stability of a separating point. We thus solve the stability issue of our previous structure. Numerical results illustrate the effectiveness of this approach.