SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

Mining Science and Technology

Volumn 19, Issue 3, 2009, Pages 385-388

  ZHANG, Hua ^a,b   WANG, Yun jia ^a,b   LI, Yong feng ^a,b  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b Jiangsu Key Laboratory of Resources and Environmental Information Engineering   (China)

Author keywords

artificial neural networks; least squares support vector machine; mining subsidence; probability integral method

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; BACK-PROPAGATION NEURAL NETWORKS; EMPIRICAL RISK MINIMIZATION; INTEGRAL METHOD; LEARNING AND TRAINING; LEAST SQUARES SUPPORT VECTOR MACHINE; LEAST SQUARES SUPPORT VECTOR MACHINES; MINING SUBSIDENCE; NEW MATHEMATICAL MODEL; NEW MODEL; STATISTICAL THEORY; SVM MODEL;

BACKPROPAGATION; CURVE FITTING; EDUCATION; IMAGE RETRIEVAL; LEAST SQUARES APPROXIMATIONS; MINING; NEURAL NETWORKS; PARAMETER ESTIMATION; PROBABILITY; PROBABILITY DENSITY FUNCTION; RELIABILITY THEORY; RESEARCH AIRCRAFT; RISK PERCEPTION; SUBSIDENCE;

SUPPORT VECTOR MACHINES;

EID: 67649399014     PISSN: 16745264     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1674-5264(09)60072-7     Document Type: Article

References (10)

1. He G.Q., Yang L., Luo Z.F., Ling G.D., Jia F.C., and Hong D. Mining Subsidence (1994), China University of Mining and Technology Press, Xuzhou (In Chinese).
2. Ren R.H. Surface movement regularity of super-wide mining face with top-coal caving. Journal of China University of Mining & Technology 15 1 (2005) 68-71
3. Guo W.B., Deng K.Z., and Zou Y.F. Artificial neural network model for predicting parameters of probability-integral method. Journal of China University of Mining & Technology 33 3 (2004) 322-326 (In Chinese).
4. Yu N.F., and Yang H.C. Optimal selection of prediction parameters for probability-integral method using particle swarm optimization and BP neural network. Science of Surveying and Mapping 33 2 (2008) 78-80 (In Chinese).
5. Yan Z.G., Zhang H.R., and Du P.J. Application of SVM in analyzing the head stream of gushing water in coal mine. Journal of China University of Mining & Technology 16 4 (2006) 433-438
6. Vapnik V.N. The Nature of Statistical Learning Theory (2000), Springer-Verlag, New York
7. Vladimir C., and Ma Y.Q. Practical selection of SVM parameters and noise estimation of SVM regression. Neural Networks 17 1 (2004) 113-126
8. Cao S.G., Liu Y.B., and Wang Y.P. A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM. Journal of China University of Mining & Technology 18 2 (2008) 172-176
9. Liu W.B., Wang R.F., and Zhang X.J. Estimating coal reserves using a support vector machine. Journal of China University of Mining &Technology 18 1 (2008) 103-106
10. Yang H.C., Zhang S.B., Deng K.Z., and Du P.J. Research into a feature selection method of hyperspectral imagery using PSO and SVM. Journal of China University of Mining & Technology 17 4 (2007) 473-478