A Local Linear Wavelet Neural Network Based on a Bayesian Design Method
Kunikazu Kobayashi, Masanao Obayashi, Takashi Kuremoto · IEEJ Transactions on Electronics Information and Systems · 2009
In general, wavelet neural networks have a problem on the curse of dimensionality, i.e. hidden units to be required are exponentially increased with a high input dimension. To solve the above problem, the wavelet neural network incorporating local linear model is proposed. On the network design, however, the number of hidden units are determined by trial and error. In the present paper, a design method based on Bayesian method is proposed for the local linear wavelet neural network. Through computer simulation, the performance of the proposed method is evaluated.