Natural Safety Prediction of Non-coal Mine Accident Based on BP Neural Network
Mine disaster system has the typical non-linear features. The traditional, previously function-setting evaluation methods and prediction methods have appeared their limitations. The BP neural network, with the nonlinear dynamic characteristics, eliminated the drift value brought about by manmade factors during the weight determination using the previous method. It is a promising natural safe-forecasting method. First, obtain the network weight parameters meets the convergence conditions through studying the known samples. Then using them as foundation to calculate mine forecast indicator system parameters, made safety prediction of forecast mines. The error between BP calculated predictive value and the actual value range from 2.22 to 5.54 percent, which showed that the training model is more accurate and reliable to forecast. The study contents have important guiding significance to mine safety management and scientific decision-making.
mine disaster system accident prediction neural network prediction indez
Wang Dan Zhou Keping Chen Qingfa
The schoool of resources and safety engineering Central South University Changsha, Hunan College of resources and environment Guangxi University Nanning, Guangxi
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
1116-1119
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）