Mine roadway field strength prediction based on improved convolutional neural network

In order to solve the problems of the complex modeling process, high computational complexity, and low prediction accuracy of the existing field strength prediction models, a mine roadway field strength prediction model based on improved convolutional neural network(CNN) is proposed. By analyzing the influence factors of electromagnetic wave transmission in large-scale fading channels in mines, using antenna operating frequency, roadway cross-sectional dimensions, roadway wall roughness, roadway wall inclination, roadway wall relative permittivity and transceiver distance as model inputs, using the electromagnetic wave propagation path loss as model outputs, the model is able to predict the changes of the roadway field strength. The improved CNN adds batch normalization layer after each convolutional layer to replace the original pooling layer so as to avoid the loss of data characteristics due to down-sampling of the pooling layer, to keep the output of each convolutional layer similarly distributed, to improve the network generalization capacity and to speed up the network convergence. The simulation results show that compared with the field strength prediction models based on CNN, BP neural network and support vector machine, the model has high consistency between the predicted value and the actual value, has stronger robustness, and improves the accuracy of mine roadway field strength prediction effectively.