基于灰关联分析和神经网络的煤与瓦斯突出预测  

Coal and gas outburst forecast based on ANN and grey correlation

作　　者：丁华[1] 王剑 王彬[3] 

机构地区：[1]太原理工大学理学院,山西太原030024 [2]山西省煤炭票证管理中心,山西太原030006 [3]中国矿业大学应用技术学院,江苏徐州221008

出　　处：《西安科技大学学报》2009年第2期136-139,共4页Journal of Xi’an University of Science and Technology

基　　金：国家自然科学基金项目(50274066);国家973计划项目(2005CB221503)

摘　　要：应用灰色关联分析,对煤与瓦斯突出影响因素进行灰关联分析,得出了各影响因素对煤与瓦斯突出影响程度的大小排序;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;用我国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进行验证。结果证明该系统能较准确地预测煤矿的瓦斯突出情况。This paper applied grey correlation analysis to analyze grey correlation about influence factors of coal and gas outburst, and got the order arrange of each influence factor according to the influence degree of coal and gas outburst. Choosing five advantage factors of grey correlation analysis as the input parameters, neural network forecasting model of coal and gas outburst was built. The network was trained by using the study samples from instances of typical coal and gas outburst mines in China, and coal and gas outburst instances of Yunnan Enhong mine were used as forecasting samples. Comparing the results from network forecasting with the results of the traditional methods, it proved that this method can meet the forecasting requirement of coal and gas outburst.

关 键 词：煤与瓦斯突出 灰关联分析 神经网络 

分 类 号：TD712.5[矿业工程—矿井通风与安全]