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
Moody J, Darken CJ (1989) Fast learning in networks of locally tuned processing units. Neural Comput 1:281–294
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
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
Draper NR, Smith H (1981) Applied regression analysis. Wiley, New York
Reas C, Fry B (2007) Processing. A programming handbook for visual designers and artists. MIT Press, Cambridge MA
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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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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