矿井涌水水源识别的MMH支持向量机模型  

MMH SUPPORT VECTOR MACHINES MODEL FOR RECOGNIZING MULTI-HEADSTREAM OF WATER INRUSH IN MINE

作　　者：闫志刚[1,2] 白海波[2] 

机构地区：[1]中国矿业大学环测学院,江苏徐州221116 [2]中国矿业大学深部岩土力学与地下工程国家重点实验室,江苏徐州221008

出　　处：《岩石力学与工程学报》2009年第2期324-329,共6页Chinese Journal of Rock Mechanics and Engineering

基　　金：国家重点基础研究发展计划(973)项目(2007CB209406);国家自然科学基金重点项目(50634050);国家自然科学基金资助项目(40802061);中国博士后科学基金(20080441081)

摘　　要：提出一种新的多水源判别的H支持向量机模型。推导H支持向量机的理论推广误差公式,发现确保高优先级节点的推广性能是提高H支持向量机性能的有效途径;设计基于SVM最大间隔逐层分类、最小间隔逐层聚类构造H支持向量机的新方法,以各支持向量机节点的分类间隔为分类、聚类指标,通过TopDown,BottomUp两种方式混合构造H支持向量机,即MMH支持向量机。实验效果表明,MMH支持向量机结构简单、泛化能力强,不仅能正确区分各类水源,而且其层次结构能很好地反映各水源的层次关系。判别函数的法向量还可以指示各含水层水质化验指标的权重,为矿井涌水水源识别提供了新的科学方法。A novel hierarchy support vector machines（H-SVMs） model is presented to recognize the headstreams of water inrush in coal mine. Firstly, an analytical model is deduced to analyze the generalization power of H-SVMs. According to the results, a feasible approach is put forward to improve the performance of H-SVMs to guarantee the performances of each SVM node, whose position is located at a high level. Secondly, a novel method is presented to build H-SVMs, i.e. MMH-SVMs（maximal margin hierarchical SVMs）, taking the separating margins of each SVM node as indices for classification and clustering, using TopDown and BottomUp routes from top to bottom to classify the input samples at each SVM node by maximal separating margin and from bottom to top clustering the input samples by minimal separating margin. Experimental results show MMH-SVMs have a simple structure, and a good generalization performance. It can predict the headstreams of water inrush correctly; and its tree structure can also denote the hierarchy of headstreams. Moreover, the normal vector parameter W in each SVM decision function can describe the weights of discrimination indices of the headstreams of water inrush, in which a novel scientific method is introduced to predict the headstream of water inrush in coal mine.

关 键 词：采矿工程 矿井涌水 多水源识别 

分 类 号：TD45[矿业工程—矿山机电]