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Mapping of radial basis function networks to partial tree shape parallel neurocomputer

  • Part VIII: Implementations
  • 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

Mapping of radial basis function network with a hybrid learning method is presented for a partial tree shape neurocomputer. The learning stage is divided into three separate parts, namely K-means clustering, P-nearest neighbor heuristic and weight value determination. The production mode consists of one part. The time complexity is given for each step to illustrate the mapping performance. The analysis shows that radial basis function networks allow efficient parallel implementations.

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References

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Authors and Affiliations

  1. Signal Processing Laboratory, Tampere University of Technology, P.O. Box 533, FIN-33101, Tampere, Finland

    Pasi Kolinummi, Timo Hämäläinen & Jukka Saarinen

Authors
  1. Pasi Kolinummi
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  2. Timo Hämäläinen
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  3. Jukka Saarinen
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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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Kolinummi, P., Hämäläinen, T., Saarinen, J. (1997). Mapping of radial basis function networks to partial tree shape parallel neurocomputer. 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/BFb0020324

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  • DOI: https://doi.org/10.1007/BFb0020324

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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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