A function approximating model for rock based on the least square support vector machines

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	A function approximating model for rock based on the least square support vector machines
	Chen, Dongfang 3; Chen, Bingrui 1; Zhao, Xiaojun 2; Zhao, Hongbo 2
刊名	DISASTER ADVANCES
	2013
卷号	6 页码:350-355
关键词	Rock material Constitutive model Least square method Support vector machines
ISSN号	0974-262X
英文摘要	A great deal of samples were needed for training an artificial neural network and making it become a good "implicit" model to describe expression mechanical property of rock material, while a small number of samples only were obtained usually in laboratory testing or practical engineering. So a function approximating model based on least squares support vector machines was proposed to solve the question above. The proposed model, which was verified by testing data of marble in Jinping II hydropower station, China, was not only a good approximation of the constitutive relation and had a satisfying predicted results and convergence velocity. The proposed model has generalization capability and can provide a reference for the application of numerical methods in rock environment engineering.
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
语种	英语
出版者	DISASTER ADVANCES
WOS记录号	WOS:000317246200046
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/3432]
专题	岩土力学所知识全产出_期刊论文国家重点实验室知识产出_期刊论文
作者单位	1.Chinese Acad Sci, State Key Lab Geomech & Geotech Engn, Inst Rock & Soil Mech ; 2.Henan Polytech Univ, Sch Energy Sci & Engn; 3.Northeastern Univ, Sch Resources & Civil Engn
推荐引用方式 GB/T 7714	Chen, Dongfang,Chen, Bingrui,Zhao, Xiaojun,et al. A function approximating model for rock based on the least square support vector machines[J]. DISASTER ADVANCES,2013,6:350-355.
APA	Chen, Dongfang,Chen, Bingrui,Zhao, Xiaojun,&Zhao, Hongbo.(2013).A function approximating model for rock based on the least square support vector machines.DISASTER ADVANCES,6,350-355.
MLA	Chen, Dongfang,et al."A function approximating model for rock based on the least square support vector machines".DISASTER ADVANCES 6(2013):350-355.