Application of general regression neural network in prediction of coal ash fusion temperature

A general regression neural network (GRNN) was employed to model the coal ash fusion temperature for obtaining better predicting performance. The coal ash compositions were employed as the inputs of GRNN, and the measured ash fusion temperature were used as the outputs of the neural network. The modeling work employing the back-propagation neural network (BPNN) was also conducted to make a comparison with the GRNN. The results show that the maximum predicting error of GRNN was 2.81%, and that of BPNN was 3.62%. Compared to BPNN, the predicting result of GRNN is more accurate for the ash fusion temperature prediction. For GRNN has the learning ability in small training sample size, it can give better predicting and generalization performance under various conditions. The design of GRNN is simpler than that of BPNN, and the calculation time needed by GRNN for convergence is significantly shorter than that needed by BPNN.