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Proceedings Paper

Hebbian- and anti-Hebbian-type neural network for blind separation of nonstationary signals
Author(s): Anke Meyer-Baese
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Paper Abstract

This contribution describes a neural network that self- organizes to recover the original signals from 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 non- stationary. 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 correlated with each other.

Paper Details

Date Published: 21 March 2001
PDF: 8 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421166
Show Author Affiliations
Anke Meyer-Baese, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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