摘要
冲击地压受到多种复杂因素的影响，对其危险性进行预测可看成非线性、高维数、小样本的多类模式识别问题。利用最近发展的新的机器学习方法——支持向量机，提出了冲击地压危险性预测的最小二乘支持向量机方法，建立了预测模型，很好地表达了冲击地压危险等级与其影响因素之间的非线性关系。算例结果表明，该预测方法是可行的，且可以获得较高的准确度。
The rock burst is affected by many complex factors, so the forecast of the degree of the rock burst is a nonlinear, high dimensional, multiclass pattern recognition with small samples. Based on statistical learning theory and complying with the minimization of structure risk, a new machine learning tool--support vector machine, which can solve the problems for multidimensional functions and has good extrapolating ability at small samples occasions, and that fetches up the ANN＇s insufficiencies, is employed. In order to improve the training velocity and prediction accuracy, this paper presents a new method for forecasting rock burst based on least square support vector machine, and constructs the prediction model. The rock burst＇s influence factors are mining depth, having or no pillar coal, rock character of top plate, intricacy degree of architectonic state, coal seam pitch, thickness of coal seam, mining system, workface by blasting or vertical exploitation. The complicated nonlinear relationship between the degree of rock burst and its affected factors is shows that the method is feasible and precise. presented. The application to the practical engineering
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2005年第A01期4881-4886,共6页
Chinese Journal of Rock Mechanics and Engineering
关键词
岩石力学
冲击地压
撮小平方支持向量机
机器学习
模式识别
rock mechanics
rock burst
least square support vector machine
machine leaming
pattern recognition
