基于MIV-PSO-BPNN的掘进面风温预测方法

Prediction method of heading face wind temperature based on MIV-PSO-BPNN

目的为防治矿井热害,解决矿井掘进面风温预测问题,方法提出一种MIV算法优化的PSO-BPNN预测模型。通过利用MIV算法确定模型的输入变量,以BP网络建模,使用粒子群优化算法结合BP神经网络实现掘进工作面风流温度的预测,得到预测结果并与BPNN模型、PSO-BPNN模型、SVR模型相比较。结果结果表明:MIV-PSO-BPNN预测模型的相对误差为-0.47%~1.81%,分别优于PSO-BPNN、BPNN、SVR预测模型的-3.96%~1.93%,-5.54%~2.98%,-2.16%~2.95%,预测模型的误差为-0.1~0.5℃,表明预测值与实测值基本一致;与BPNN预测模型、PSO-BPNN预测模型、SVR预测模型相比,MIV-PSO-BPNN预测模型的预测结果平均绝对误差分别减少65%,54%,50%,均方误差分别减少88%,78%,69%,表明该预测模型的预测效果优于其他3种模型。结论所提模型适用于矿井掘进工作面风温的预测。

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 algorithm 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 superior 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%,respectively,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.