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Volumn 20, Issue 5, 2007, Pages 13-18

Landslide displacement prediction based on Takens theory and SVM

Author keywords

Landslide displacement prediction; Phase space reconstruction; Road engineering; Support vector machine; Takens theory

Indexed keywords

NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RECONSTRUCTION (STRUCTURAL); SUPPORT VECTOR MACHINES;

EID: 35548959959     PISSN: 10017372     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (8)

References (13)
  • 3
    • 33644944381 scopus 로고    scopus 로고
    • Calculation method of foundation bearing capacity based on division in loess area for highway engineering
    • SHI Gang, WANG Jin-guo, ZHI Xi-lan, et al. Calculation Method of Foundation Bearing Capacity Based on Division in Loess Area for Highway Engineering [J]. Journal of Traffic and Transportation Engineering, 2005, 5(4): 48-52.
    • (2005) Journal of Traffic and Transportation Engineering , vol.5 , Issue.4 , pp. 48-52
    • Shi, G.1    Wang, J.-G.2    Zhi, X.-L.3
  • 7
    • 0141908274 scopus 로고    scopus 로고
    • Analytic method and application about chaotic slope deformation destruction time-series
    • FU Yi-xiang, LIU Zhi-qiang. Analytic Method and Application About Chaotic Slope Deformation Destruction Time-series [J]. Journal of Wuhan University of Technology, 2003, 27(4): 474-476.
    • (2003) Journal of Wuhan University of Technology , vol.27 , Issue.4 , pp. 474-476
    • Fu, Y.-X.1    Liu, Z.-Q.2
  • 9
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • CHERKASSKY V, MA Y. Practical Selection of SVM Parameters and Noise Estimation for SVM Regression [J]. Neural Networks, 2004, 17(1): 113-126.
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 11
    • 0346881149 scopus 로고    scopus 로고
    • Experimentally optimal V in support vector regression for different noise models and parameter settings
    • CHALIMOURDA C, SCHOLKOPF B, SMOLA A. Experimentally Optimal V in Support Vector Regression for Different Noise Models and Parameter Settings [J]. Neural Networks, 2004, 17(1): 127-141.
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 127-141
    • Chalimourda, C.1    Scholkopf, B.2    Smola, A.3
  • 13
    • 1542427746 scopus 로고    scopus 로고
    • Research on nonlinear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm
    • LIU Kai-yun, QIAO Chun-sheng, TENG Wen-yan. Research on Nonlinear 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.
    • (2004) Chinese Journal of Geotechnical Engineering , vol.26 , Issue.1 , pp. 57-61
    • Liu, K.-Y.1    Qiao, C.-S.2    Teng, W.-Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.