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.
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
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)
关键词
急倾斜煤层
顶煤可放性
判别分析
支持向量机
steep seam
top coal caving capability
discriminant analysis
support vector machines（SVM）
