基于MIV-PSO-BPNN的掘进面风温预测方法OA北大核心CSTPCD

Objectives To prevent and control thermal damage in mines and solve the problem of predicting wind temperature in mining excavation faces,Methods a PSO-BPNN prediction model optimized by MIV algo-rithm was proposed.The MIV algorithm was used to determine the input variables for the model,followed by BP network modeling.The particle swarm optimization algorithm combined with the BP neural network was then employed to predict the airflow temperature of the excavation working face.The predicted results were compared with those from the BPNN model,PSO-BPNN model,and SVR model.Results The results showed that the relative error range of the MIV-PSO-BPNN prediction model was-0.47%to 1.81%,which was supe-rior to the PSO-BPNN,BPNN,and SVR prediction models with ranges of-3.96%to 1.93%,-5.54%to 2.98%,and-2.16%to 2.95%,respectively.The prediction error was between-0.1℃and 0.5℃,indicating that the predicted values and tested values were basically consistent;Compared to the BPNN,PSO-BPNN,and SVR prediction models,the MIV-PSO-BPNN model's average absolute error decreased by 65%,54%,and 50%,re-spectively,and the mean square error had decreased by 88%,78%,and 69%,respectively.This demonstrated that the prediction effect of the MIV-PSO-BPNN model was superior to the other three models.Conclusions The proposed model was suitable for predicting the air temperature of mining excavation working faces.