Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms

Authors:
Kai Huang;Le Wang;Jinwen Ma
Affiliations:
Department of Information Science, School of Mathematical Sciences, And LMAM, Peking University, Beijing, 100871, China;Department of Information Science, School of Mathematical Sciences, And LMAM, Peking University, Beijing, 100871, China;Department of Information Science, School of Mathematical Sciences, And LMAM, Peking University, Beijing, 100871, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Radial basis function (RBF) networks of Gaussian activation functions have been widely used in many applications due to its simplicity, robustness, good approximation and generalization ability, etc.. However, the training of such a RBF network is still a rather difficult task in the general case and the main crucial problem is how to select the number and locations of the hidden units appropriately. In this paper, we utilize a new kind of Bayesian Ying-Yang (BYY) automated model selection (AMS) learning algorithm to select the appropriate number and initial locations of the hidden units or Gaussians automatically for an input data set. It is demonstrated well by the experiments that this BYY-AMS training method is quite efficient and considerably outperforms the typical existing training methods on the training of RBF networks for both clustering analysis and nonlinear time series prediction.