Blind separation of signals with mixed kurtosis signs using threshold activation functions

A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems 2001-05, Vol.12 (3), p.618-624
Hauptverfasser: Mathis, H., von Hoff, T.P., Joho, M.
Format: Artikel
Sprache:eng
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Zusammenfassung:A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most other nonlinear functions used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.925565