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Blind source separation of nonlinear mixing models

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3 Author(s)
Te-Won Lee ; Salk Inst., San Diego, La Jolla, CA, USA ; Koehler, B.-U. ; Orglmeister, R.

We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms

Published in:

Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop

Date of Conference:

24-26 Sep 1997

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