Establishment of grey-neural network forecasting model of coal and gas outburst

AbstractEffect factors on coal and gas outburst are analyzed using grey correlation method so as to determine the input parameters of artificial neural network (ANN). Then using the improved BP algorithm, we choose five dominant factors of grey correlation analysis as the input parameters to establish neural network model for forecasting coal and gas outburst. This network was trained by using the learning samples collected from the instances of typical coal and gas outburst mines in China. Meanwhile, we take coal and gas outburst instances of Yunnan Enhong coal mine as forecasting samples and compare the forecasting result from these samples with that from the conventional method, indicating that this model can meet the forecasting requirements of coal and gas outburst