Based on the artificial neural network , the factors affecting the face pressure was set as input layers, and the BP neural network was set up, through gold segmentation algorithm defined the proper nodes, got the optimal model , and predicted the resistance pressure strength, the resistance of un-pressure strength and pressure step, analysis showed that the error controlled in the range of -10%~10%, and the error were normally distributed, through many ways to improve the prediction accuracy, the average can control the prediction accuracy in the range of -5%~5%, which can obtain a better predict accuracy, and possessed great significance to the safety production of the working face .
李小永，刘立明. 神经网络系统预测采面来压研究[J]. 煤炭与化工, 2014, 37(8): 37-40.
LI Xiao-yong，LIU Li-ming. Study on Working Face Pressure Prediction Based on Artificial Neural Network. CCI, 2014, 37(8): 37-40.
