急倾斜煤层顶煤可放性识别的支持向量机模型

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，具有预测精度高、容易实现、应用性强等优点。

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

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 has showed that the nonlinear relation between the top-coal seam caving ability and influencing factors was learned from the finite empirical data by SVM model, and the model has advantages of high precision, easy implementing and strong practicality.