煤与瓦斯突出灰色-神经网络预测模型的建立及应用  

Establishment and Application of Grey-Neural Network Forecasting Model of Coal and Gas Outburst

作　　者：苗琦 杨胜强[2] 欧晓英[2] 陈祖云[2] 

机构地区：[1]一九八煤田地质队,云南昆明650208 [2]中国矿业大学安全工程学院,江苏徐州221116

出　　处：《采矿与安全工程学报》2008年第3期309-312,317,共5页Journal of Mining & Safety Engineering

基　　金：云南省科技厅攻关项目(2005IT02);国家自然科学基金项目(50274066)

摘　　要：对煤与瓦斯突出影响因素进行灰关联分析,以此确定人工神经网络的输入参数.并应用改进的BP算法,选择灰关联分析的5个优势因子作为输入参数,建立了煤与瓦斯突出预测的神经网络模型.选用典型突出矿井的煤与瓦斯突出实例作为学习样本,对网络进行训练学习,并以云南恩洪煤矿的煤与瓦斯突出实例作为预测样本,将经过网络预测的结果与传统方法的计算结果进行对比.结果表明该灰色-神经网络模型能够满足煤与瓦斯突出预测的要求.Grey correlation analysis was made with respect to factors affecting coal and gas outburst and the input parameters of artificial neural network （ANN） determined. Then five dominant factors were chosen for grey correlation analysis as the input parameters based on the improved BP algorithm, and neural network forecasting model of coal and gas outburst established. The network was trained by using the study samples from the instances of typical coal and gas outburst mines, and coal and gas outburst instances of Yunnan Enhong mine were used as forecasting samples. The comparison between the results from network forecasting with that of the traditional methods indicates that this method can meet the requirement for coal and gas outburst forecast.

关 键 词：煤与瓦斯突出 灰关联分析 灰色-神经网络 

分 类 号：TD712[矿业工程—矿井通风与安全]