LIU Kaiyun, QIAO Chunsheng, TENG Wenyan. Research on non-linear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm[J]. Chinese Journal of Geotechnical Engineering, 2004, 26(1): 57-61.
Citation:
LIU Kaiyun, QIAO Chunsheng, TENG Wenyan. Research on non-linear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm[J]. Chinese Journal of Geotechnical Engineering, 2004, 26(1): 57-61.
LIU Kaiyun, QIAO Chunsheng, TENG Wenyan. Research on non-linear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm[J]. Chinese Journal of Geotechnical Engineering, 2004, 26(1): 57-61.
Citation:
LIU Kaiyun, QIAO Chunsheng, TENG Wenyan. Research on non-linear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm[J]. Chinese Journal of Geotechnical Engineering, 2004, 26(1): 57-61.
1.School of Civil Engineering and Architecture Beijing Jiaotong University Beijing 100044 China 2.Department of Civil Engineering Shijiazhuang Institute of Railway Engineering Shijiazhuang 050041 China
Based on the Structural Risk Minimization principle,the latest data mining method in artificial intelligence field—support vector machine algorithm was introduced in this paper.A program was worked out in language Matlab for a slope engineering project by using different kernel function.Compared with the result obtained by using the Artificial Neural Network algorithm based on the Empirical Risk Minimization principle,the SVM algorithm is obviously superior to the ANN algorithm whatever on machine learning or prediction accuracy and it can be used to practical engineering.
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