Blind source separation based on self-organizing neural network

Authors:
Anke Meyer-Bäse;Peter Gruber;Fabian Theis;Simon Foo
Affiliations:
Department of Electrical Engineering, Florida State University, Tallahassee, FL 32310-6046, USA;Department of Electrical Engineering, Florida State University, Tallahassee, FL 32310-6046, USA and Institute of Biophysics, University of Regensburg, Regensburg 93040, Germany;Department of Electrical Engineering, Florida State University, Tallahassee, FL 32310-6046, USA and Institute of Biophysics, University of Regensburg, Regensburg 93040, Germany;Department of Electrical Engineering, Florida State University, Tallahassee, FL 32310-6046, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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Abstract

This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required about the statistical properties of the sources and the coefficients of the linear transformation, except the fact that the source signals are statistically independent and nonstationary. This is often true for real life applications. We propose an online learning solution using a neural network and use the nonstationarity of the sources to achieve the separation. The learning rule for the network's parameters is derived from the steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other. In this process divide the problem into two learning problems one of which is solved by an anti-Hebbian learning and the other by an Hebbian learning process. We also compare the performance of our algorithm with other solutions to this task.