Coal ash fusion temperature forecast based on Gaussian regularization RBF neural network

Gaussian regularization is an effective method to improve the generalization ability of neural networks. A Gaussian regularization RBF neural network (GRNN) which combines the advantages of RAN, and regularization is proposed in this paper. And a model using GRNN is presented to predict the ash fusion temperature (AFT) for some Chinese coals Compared with the traditional techniques, the GRNN prediction model has not only small training and testing error, but also a more compact network structure.