EVALUATION OF NEURAL NETWORKS BASED ON RADIAL BASIS FUNCTIONS AND THEIR APPLICATION TO THE PREDICTION OF BOILING POINTS FROM STRUCTURAL PARAMETERS

The performance of neural networks based on radial basis functions (RBF neural networks) is evaluated. The network uses modified Gaussian kernel functions which have shown better results for classification purposes. RBF networks are tested for the variation of some design parameters and the presence of noise in the sample data. The problems of generalization and extrapolation are addressed, and a procedure is suggested for how to test for the generalization ability of neural networks. An RBF network has been applied to chemical data in order to estimate boiling points at normal pressure from structural parameters. The results show a significant decrease of the prediction error when compared to results obtained by multiple linear regression.