急倾斜煤层顶煤可放性识别的支持向量机模型  

Support vector machine model for distinguishing the difficulty degree of top-coal caving in steep seam

作　　者：刘年平[1] 王宏图[1,2] 袁志刚[1] 

机构地区：[1]重庆大学西南资源开发及环境灾害控制工程教育部重点实验室,重庆400030 [2]重庆大学复杂煤气层瓦斯抽采国家地方联合工程实验室,重庆400030

出　　处：《煤炭学报》2010年第11期1859-1862,共4页Journal of China Coal Society

基　　金：国家自然科学基金资助项目(50774106);国家重点基础研究发展计划(973)资助项目(2005CB221502);国家自然科学创新群体基金资助项目(50621403);川煤集团科技项目资助项目(2009-08)

摘　　要：分析了急倾斜煤层巷道放顶煤开采顶煤可放性的主要影响因素,在巷道放顶煤工业性试验及开采实践经验数据分析的基础上,将基于结构风险最小化原理的支持向量方法用于急倾斜煤层顶煤可放性识别问题中,建立了基于径向基核函数的可放性识别支持向量机模型,并将该模型用于工程实例检测。研究表明,该模型能通过有限经验数据的学习,建立顶煤可放性与影响因素之间的非线性关系。Analyzed the main factors of top coal caving of roadway sub-level caving mining in steep seam.On the basis of analysis of the experience and data on the test and mining examples of roadway sub-level caving in steep seam,support vector machines（SVM）analysis model for distinguishing the difficulty degree of top-coal caving in steep seam were established through radial basis kernel function,which based on structural risk minimization principle,then the model was applied to engineering examples.The study shows that the nonlinear relation between the top-coal seam caving ability and influencing factors is learned from the finite empirical data by SVM model.

关 键 词：急倾斜煤层 顶煤可放性 判别分析 支持向量机 

分 类 号：TD823.213[矿业工程—煤矿开采]