TEMPERATURE-CONSTRAINED BACKPROPAGATION NEURAL NETWORKS

A temperature-constrained training algorithm has been devised for back propagation neural networks. The use of a temperature constraint softe ns the neural network modeling. This algorithm furnishes more stable m odels and may decrease training time substantially. These networks are resistant to overfitting and avoid the problem of overtraining. Tempe rature-constrained and conventional backpropagation networks are evalu ated with a synthetic data set and mass spectra from compounds whose m olecular formulas are C8H18O. Confusion matrices are used for evaluati ng classification performance.