Abstract:
Objective To compare the pros and cons of back propagation (BP) neural network (BPNN) and radial basis function (RBF) neural network (RBFNN) in prediction performance on working age of pneumoconiosis occurrence in coal workers.
Methods BPNN and RBFNN were constructed using SPSS 19.0 software. Root of mean square error, average relative error, and mean absolute error were applied to compare the predicting outcomes of the two models.
Results Combined the predicted values and the true values in the same scatter diagram, the distribution of values predicted by the BPNN model was concentrated around a 45-degree line, indicating an approximate shape of the ideal state; but the distribution of values predicted by the RBFNN model was in disorder. There was no significant difference between the true value and the predicted values by the BPNN model or the RBFNN model (t=0.530 and 0.231 respectively, P=0.596 and 0.817 respectively). The root of mean square error, average relative error, and mean absolute error of the RBFNN model and the BPNN model were 3.51 and 1.89, 0.12 and 0.06, 2.76 and 1.42, respectively.
Conclusion For the working age of pneumoconiosis occurrence in coal workers, the prediction performance of BPNN is superior to RBFNN, owing to its better fitting to true values and prediction accuracy.
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