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Learning algorithm of the revised RBF network and its application to the media art system

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Abstract

In this study, a revised radial basis function (RBF) network is proposed and applied to the identification problems of a nonlinear system and a media art system. In the revised RBF network, the structural parameters such as the means and variances of the radial basis functions in the neurons are determined automatically, and so the revised RBF network can easily be applied to practical complex problems such as the media art system. The media art system outputs art expressions such as sound and graphics using the artificial sensibility surfaces that are identified using the revised RBF network.

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References

  1. Moody J, Darken CJ (1989) Fast learning in networks of locally tuned processing units. Neural Comput 1:281–294

    Article  Google Scholar 

  2. Kudo T, Kondo T, Nakagawa M, et al (2009) Medical image recognition of the white and gray matter of the brain by a radial basis function (RBF) neural network. Proceedings of the 40th ISCIE International Symposium on Stochastic Sytems Theory and its Applications, pp 259–263

  3. Kondo T, Pamdya AS, Nagashino H (2007) GMDH-type neural network algorithm with a feedback loop for structural identification of an RBF neural network. Int J Knowledge-Based Intel Eng Syst 11:157–168

    Google Scholar 

  4. Draper NR, Smith H (1981) Applied regression analysis. Wiley, New York

    MATH  Google Scholar 

  5. Reas C, Fry B (2007) Processing. A programming handbook for visual designers and artists. MIT Press, Cambridge MA

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Media Design, Keio University, Yokohama, Japan

    Chihiro Kondo

  2. Graduate School of Health Sciences, University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan

    Tadashi Kondo

Authors
  1. Chihiro Kondo
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  2. Tadashi Kondo
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Corresponding author

Correspondence to Tadashi Kondo.

Additional information

This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010

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Cite this article

Kondo, C., Kondo, T. Learning algorithm of the revised RBF network and its application to the media art system. Artif Life Robotics 15, 258–263 (2010). https://doi.org/10.1007/s10015-010-0804-9

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  • DOI: https://doi.org/10.1007/s10015-010-0804-9

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