煤矿井筒变形混沌特征及预报模型研究  

Study on Coal Mine Shaft Chaos Characteristics and Deformation Prediction Model

作　　者：唐艳梅[1,2,3] 王坚[1,2,3] 彭祥国[1,2,3] 王建鹏[1,2,3] 

机构地区：[1]中国矿业大学 [2]国土环境与灾害监测国家测绘局重点实验室 [3]江苏省资源环境信息工程重点实验室

出　　处：《金属矿山》2011年第2期149-152,共4页Metal Mine

基　　金：中国矿业大学煤炭资源与安全开采国家重点实验室开放研究基金项目(编号:08KF07);国家自然科学基金项目(编号:40904004)

摘　　要：井筒灾害预报预警是煤矿安全生产的重要保证,通过对兖州某矿井筒变形监测数据的分析,提出利用最大Lyapunov指数和关联维数描述系统的混沌特征,计算嵌入维数,并用于确定支持向量机(SVM)模型的输入。采用支持向量机模型建立变形—时间关系,由内、外符合精度检验模型精度及可靠性。通过同BP神经网络模型预测结果的比较发现,支持向量机模型既具有较好的拟合能力,同时也具有较好的预测能力。本研究对井筒灾害监测与治理具有重要指导意义。The forecasting and pre-warning for shaft disaster is an important guarantee for safety production of coal mine. Based on the analysis of deformation monitoring data of a Yanzhou mine shaft well,it is proposed to adopt maximum Lyapunov index and correlation dimension to describe the system＇s chaos characteristics. The embedding dimension is im- plied to determine the input dimension of SVM model in order to establish the relationship of deformation and time,the inside and outside precision is introduced to evaluate the model＇s accuracy and reliability. Compared to the prediction results of the BP neural network,it is found that the SVM model has good fitting capability as well as good predicting ability. This study has an important guiding significance to disaster monitoring and treatment of the main shaft.

关 键 词：井筒 SVM 变形预报 嵌入维 混沌特征 

分 类 号：TD7[矿业工程—矿井通风与安全]