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A self-organizing network that can follow non-stationary distributions

  • Part IV:Signal Processing: Blind Source Separation, Vector Quantization, and Self Organization
  • Conference paper
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

A new on-line criterion for identifying “useless” neurons of a self-organizing network is proposed. When this criterion is used in the context of the (formerly developed) growing neural gas model to guide deletions of units, the resulting method is able to closely track nonstationary distributions. Slow changes of the distribution are handled by adaptation of existing units. Rapid changes are handled by removal of “useless” neurons and subsequent insertions of new units in other places.

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References

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Author information

Authors and Affiliations

  1. Systembiophysik, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780, Bochum, Germany

    Bernd Fritzke

Authors
  1. Bernd Fritzke
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Fritzke, B. (1997). A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020222

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  • DOI: https://doi.org/10.1007/BFb0020222

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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