关键词: 主成分分析, 神经网络, 煤与瓦斯突出, 预测

Abstract: In this paper，three main factors are extracted to replace seven original factors affecting coal and gas outburst by means of principal component analysis when variance contribution is more than 85%，by which，the input parameters of BP neural network are determined.PCA-BP neural network prediction model is established，which is trained by the study samples from typical coal and gas outburst mines.In order to check feasibility and validity of the PCA-BP model，the instances of a coal mine in Yunnan province are used as predictive samples.PCA-BP model and traditional BP neural network are compared by predictive samples.Simulation results show that the PCA-BP neural network model is superior to traditional BP neural network，and meets the requirement for coal and gas outburst prediction.

Key words: principal component analysis, neural network, coal and gas outburst, forecast