基于支持向量机的开采沉陷预计参数选取研究.pdf

Vol No CHINA MINING MAGAZINE Feb RBF TD A Study on the selection of predication parameters on mining subsidence based on support vector machine T UO Wanbing JIANG Wei WU Fengmin School of Mines and Engineering China University of Mining and Technology Yinchuan College Yinchuan China Blue Flame of Coal Bed Gas in Shanxi Group Co Ltd Jincheng China Abstract In order to establish selection model of mining subsidence predicting parameters which has self learning ability and with high accuracy In this paper using principal component analysis preprocessing the data in the literature we have established the prediction parameters of mining subsidence selection model using support vector machine based on radial basis function RBF by selecting main components factor with cumulative variance reaches of and surface subsidence factor as the input and output variables Results show under the circumstances of less training samples Support vector machine SVM model has high precision and strong generalization ability the prediction accuracy and prediction stability is better which was proved contrasting average relative error and root mean square error Key words support vector machine rincipal component analysis subsidence coefficient selection p