Quality Control Model of Ore Processing Based on Elman Neural Network

Abstract

In this paper, by establishing Elman neural network and decision tree model, the quality control of ore processing is studied and reasonably predicted.Predict the product quality and the temperature corresponding to the target quality. Elman neural network model is established, training set and output set are input for training, and test set is input into neural network to obtain the relative error of real value and training value; After verification, the relative error between our real value and training value is within 100% ± 5%, and the model is accurate; Finally, the input value is substituted to obtain the forecast data.Establish the mathematical model of product qualification rate. The decision tree model is established by using SPSS, and the decision tree system is constructed by using the four factors of system setting temperature, raw ore parameters, process data and final ore quality. Through inspection, our R 2 is 0.999991, close to 1, and the model fitting is good. Compared with other parameters, it can be found that the model has good sensitivity and high accuracy. Finally, the input data is sent into the model to get the final prediction results.