On Mine Environmental Gas Concentration Prediction Model Based on Adam Algorithm BP Neural Network
In order to solve the problems of existing mine environment gas concentration prediction methods that cannot handle large data volume,poor adaptability,large errors,and easy to fall into local optimality,an improved BP(Back Propagation)neural network model based on Adam algorithm is proposed.The model is suitable for multiple environments in mines.Under the parameters,it predicts the environmental gas concentration in a certain area.The real data collected by the monitoring system is normalized and formed into a data set,and a new network model is formed by effectively combining the Adam algorithm with the BP network model.After training and tuning the model with the training set,the loss rate stabilized after 1200 iterations,and the overall average error rate of the verification set prediction is 1.258%.The results show that the optimized model has improved the network training speed and avoided the shortcoming that the traditional BP model is easy to fall into local minimum,and meantime it reduces the relative error of prediction.
gas concentrationBP neural networkactivation functionAdam algorithmvanishing gradient
NETL
NSTL
万方数据
维普
