基于GA-BP神经网络的矿井粉尘浓度预测研究

为了准确预测矿井粉尘浓度,有效防治矿井粉尘危害,运用遗传算法优化的BP神经网络预测模型(GA-BP模型)对某矿山工作面时间序列粉尘浓度进行预测,以预测结果的相对误差、平均绝对百分比误差来评判模型的预测准确性。再利用BP神经网络预测模型,卷积神经网络预测模型(CNN模型)的预测结果同GA-BP预测模型的预测结果进行对比验证,以均方根误差来评价三种模型的预测效果。结果表明,应用GA-BP预测模型,相对误差最大为4.27%,最小为0.14%,相对误差都在10%以内,预测样本的平均绝对百分比误差(MAPE)小于10%,达到了高精度预测要求。CNN、BP、GA-BP三种预测模型的RMSE值分别为1.1007、1.0008、0.9354,GA-BP预测模型对于该矿山工作面粉尘浓度预测效果最好。

Prediction of mine dust concentration based on GA-BP neural network

In order to accurately predict the mine dust concentration and effectively prevent the mine dust hazards, the BP neural network prediction model (GA-BP model) optimized by genetic algorithm is used to predict the dust concentration of a mine working face time series. The prediction accuracy of the model is evaluated by the relative error and the average absolute percentage error of the prediction results. By using the BP neural network prediction model, the prediction results of the convolutional neural network prediction model (CNN model) and the GA-BP prediction model were compared and verified, and the root-mean-square error was used to evaluate the prediction effect of the three models. The results show that the maximum relative error of GA-BP prediction model is 4.27%, the minimum is 0.14%, the relative error is less than 10%, the average absolute percentage error (MAPE) of the predicted sample is less than 10%, which meets the requirement of high precision prediction. The RMSE values of CNN, BP and GA-BP are 1.1007, 1.0008 and 0.9354, respectively. The GA-BP prediction model has the best effect on the dust concentration prediction of the mine working face.