Abstract
The quality of the response of a RBF neural network depends strongly on the calculation method of the centres and the variance matrices. This paper describes an algorithm which combines the calculation of the centres and variances of the Gaussian nodes to improve the response of a RBF neural network. The selection of the centres is made using a modified version of the K-means algorithm and the variances are based on the sample variance-covariance matrices of the input values associated with the centres. Applications to classification and function approximation problems are considered.
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References
Dempster A P., Laird N M and Rubin D B: Maximum Liklehood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society B 39, 1 pp 1–38, 1977.
Bishop C M: Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995.
Godjevac J: Neuro-Fuzzy Controllers, Design and Application, Presses Polytechniques et Universitaires Romandes, Lausanne, 1997
Moody J and Darken C J: Fast Learning in Networks of Locally-tuned Processing Units. Neural Computation 1, 2, pp 281–294, 1989.
Steele N., Godjevac J: Adaptive Radial Basis Fumction Neural Networks and Fuzzy Systems. Proc CES A′96 Symposium on Discrete Events and Manufacturing Systems, Lille, France, pp 143–148, 1996.
Babuska R: Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston, 1998.
Albrecht R., Werner W: Ein Verfahren zur Identifizierung von Zeichen, deren Wiedergabe sta-tionaeren statistischen Stoerungen unterworfen ist. Computing, 1, 1, pp 1–7, 1966.
Gustafson D., Kessel W: Fuzzy Clustering with a Fuzzy Covariance Matrix. Proc IEEE CDC, San Diego, CA, USA, pp 761–766, 1979.
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© 1999 Springer-Verlag Wien
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Scheibel, F., Steele, N.C., Low, R. (1999). Centre and Variance Selection for Gaussian Radial Basis Function Artificial Neural Networks. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_25
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_25
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
eBook Packages: Springer Book Archive