Centre and Variance Selection for Gaussian Radial Basis Function Artificial Neural Networks
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.
KeywordsHide Layer Fuzzy Controller Output Node Fuzzy Cluster Centre Vector
Unable to display preview. Download preview PDF.
- Bishop C M: Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995.Google Scholar
- Godjevac J: Neuro-Fuzzy Controllers, Design and Application, Presses Polytechniques et Universitaires Romandes, Lausanne, 1997Google Scholar
- 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.Google Scholar
- Gustafson D., Kessel W: Fuzzy Clustering with a Fuzzy Covariance Matrix. Proc IEEE CDC, San Diego, CA, USA, pp 761–766, 1979.Google Scholar