Application of Improved Variable Learning Rate Back Propagation Neural Network in Energy Dispersion X-Ray Fluorescence Quantitative Analysis

Artificial Neural Network (ANN) can be applied to process data in analysis of Energy Dispersive X-Ray Fluorescence (EDXRF) due to its ability of nonlinear relationship processing. However, considering the conventional Back Propagation (BP) algorithm problems of slow convergence speed and easily getting into local dinky value, an improved Variable Learning Rate Back Propagation (VLBP) neural network is proposed, based on traditional VLBP neural network algorithm, the Lagrange interpolation polynomial is considered to calculate the additional parameter of variable learning rate. In the experiment part, we compare different models in the number of iterations to achieve error precision with the same batch of lead-zinc ore samples. Besides, a 30-time stability test of the models above is implemented. The Zn element concentration of a batch of lead-zinc ore samples is predicted by using the improved algorithm and the predicted values are compared with the chemical analysis values. The results show that the relative error between them is less than 5%. Additionally, 10 groups of samples whose characteristic peak counts exceed the training sample are selected with the purpose of generalization ability test. The relative error is comparatively higher, but still less than 5%, which refers to its certain generalization ability. The results show that the improved VLBP can quickly and accurately predict the concentration of target elements in EDXRF, it has dramatically improvement in convergence speed comparing with BP and VLBP, however, stochastic gradient descent and Adadelta display a more effective way.