Roof Pressure Prediction in Coal Mine Based on Grey Neural Network

The prediction of roof pressure in mining area plays an important role in effectively preventing roof accidents and ensuring the safety of mine production. Because the roof pressure in the mine is affected by various natural and human factors, and there is a dynamic and fuzzy nonlinear relationship between the factors. At present, the lack of systematic management will seriously limit the analysis and judgment of the mine safety situation, and lead to the occurrence of mine accidents. In this paper, the compaction data of the roof of a mine return air working face in Xuzhou is taken as the experimental data, and an improved grey neural network model is proposed, which combines the grey theory with the neural network algorithm organically. It not only eliminates the shortcomings of the neural network model, but also makes up for the shortcomings of the grey network that cannot carry out self-feedback regulation. MATLAB is used to simulate and test the improved combined prediction model for roof pressure prediction, and the results are compared with those of single grey model and single BP neural network model. The simulation results show that the improved model not only improves the prediction efficiency, shortens the training time, but also improves the accuracy, so it is of great significance to the safety prediction of the mine roof.