ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 52

Stable Higher-Order Recurrent Neural Network Structures for Nonlinear Blind Source Separation

Author(s): Yannick Deville, Hosseini Sharam


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.

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