메뉴 건너뛰기




Volumn 137, Issue , 2014, Pages 165-172

Predicting dynamic deformation of retaining structure by LSSVR-based time series method

Author keywords

Ground settlement; Lateral displacement; LSSVR; Phase space reconstruction; Time series

Indexed keywords

FORECASTING; PHASE SPACE METHODS; SETTLEMENT OF STRUCTURES; TIME SERIES;

EID: 84899618956     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.03.073     Document Type: Article
Times cited : (47)

References (30)
  • 1
    • 55149114659 scopus 로고    scopus 로고
    • Finite element numerical simulation of three-dimensional seepage control for deep foundation pit dewatering
    • Luo Z.J., Zhang Y.Y., Wu Y.X. Finite element numerical simulation of three-dimensional seepage control for deep foundation pit dewatering. J. Hydrodyn. 2008, 20:596-602.
    • (2008) J. Hydrodyn. , vol.20 , pp. 596-602
    • Luo, Z.J.1    Zhang, Y.Y.2    Wu, Y.X.3
  • 2
    • 0032099284 scopus 로고    scopus 로고
    • The local minima-free condition of feedforward neural networks for outer-supervised learning
    • Huang D.S. The local minima-free condition of feedforward neural networks for outer-supervised learning. IEEE Trans. Syst. Man Cybern. B 1998, 28:477-480.
    • (1998) IEEE Trans. Syst. Man Cybern. B , vol.28 , pp. 477-480
    • Huang, D.S.1
  • 3
    • 0000759063 scopus 로고    scopus 로고
    • Radial basis probabilistic neural networks: model and application
    • Huang D.S. Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit. 1999, 13:1083-1101.
    • (1999) Int. J. Pattern Recognit. , vol.13 , pp. 1083-1101
    • Huang, D.S.1
  • 4
    • 18144390208 scopus 로고    scopus 로고
    • Estimating wall deflections in deep excavations using Bayesian neural networks
    • Chua C.G., Goh A.T.C. Estimating wall deflections in deep excavations using Bayesian neural networks. Tunn. Undergr. Sp. Technol. 2005, 20:400-409.
    • (2005) Tunn. Undergr. Sp. Technol. , vol.20 , pp. 400-409
    • Chua, C.G.1    Goh, A.T.C.2
  • 5
    • 1642618591 scopus 로고    scopus 로고
    • Neural-network-based regression model of ground surface settlement induced by deep excavation
    • Leu S.S., Lo H.C. Neural-network-based regression model of ground surface settlement induced by deep excavation. Autom. Constr. 2004, 13:279-289.
    • (2004) Autom. Constr. , vol.13 , pp. 279-289
    • Leu, S.S.1    Lo, H.C.2
  • 6
    • 57749092656 scopus 로고    scopus 로고
    • A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks
    • Huang D.S., Du J.X. A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Netw. 2008, 19:2099-2115.
    • (2008) IEEE Trans. Neural Netw. , vol.19 , pp. 2099-2115
    • Huang, D.S.1    Du, J.X.2
  • 7
    • 84866490627 scopus 로고    scopus 로고
    • A general CPL-AdS methodology for fixing dynamic parameters in dual environments
    • Huang D.S., Jiang W. A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. B 2012, 42:1489-1500.
    • (2012) IEEE Trans. Syst. Man Cybern. B , vol.42 , pp. 1489-1500
    • Huang, D.S.1    Jiang, W.2
  • 8
    • 19344364336 scopus 로고    scopus 로고
    • Zeroing polynomials using modified constrained neural network approach
    • Huang D.S., Ip H.H.S., Law K.C.K., Chi Z. Zeroing polynomials using modified constrained neural network approach. IEEE Trans. Neural Netw. 2005, 16:721-732.
    • (2005) IEEE Trans. Neural Netw. , vol.16 , pp. 721-732
    • Huang, D.S.1    Ip, H.H.S.2    Law, K.C.K.3    Chi, Z.4
  • 9
    • 3042584634 scopus 로고    scopus 로고
    • A neural root finder of polynomials based on root moments
    • Huang D.S., Ip H.H.S., Chi Z.R. A neural root finder of polynomials based on root moments. Neural Comput. 2004, 16:1721-1762.
    • (2004) Neural Comput. , vol.16 , pp. 1721-1762
    • Huang, D.S.1    Ip, H.H.S.2    Chi, Z.R.3
  • 10
    • 2442503670 scopus 로고    scopus 로고
    • A constructive approach for finding arbitrary roots of polynomials by neural networks
    • Huang D.S. A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans. Neural Netw. 2004, 15:477-491.
    • (2004) IEEE Trans. Neural Netw. , vol.15 , pp. 477-491
    • Huang, D.S.1
  • 11
    • 0343586640 scopus 로고    scopus 로고
    • The united adaptive learning algorithm for the link weights and shape parameter in RBFN for pattern recognition
    • Huang D.S. The united adaptive learning algorithm for the link weights and shape parameter in RBFN for pattern recognition. Int. J. Pattern Recognit. 1997, 11:873-888.
    • (1997) Int. J. Pattern Recognit. , vol.11 , pp. 873-888
    • Huang, D.S.1
  • 12
    • 0141425890 scopus 로고    scopus 로고
    • Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach
    • Sivakumar B. Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach. Environ. Model. Softw. 2003, 18:721-728.
    • (2003) Environ. Model. Softw. , vol.18 , pp. 721-728
    • Sivakumar, B.1
  • 13
    • 79956368545 scopus 로고    scopus 로고
    • Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs
    • Dhanya C.T., Kumar D.N. Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs. J. Hydrol. 2011, 403:292-306.
    • (2011) J. Hydrol. , vol.403 , pp. 292-306
    • Dhanya, C.T.1    Kumar, D.N.2
  • 15
    • 78049509420 scopus 로고    scopus 로고
    • Translation invariant morphological time-lag added evolutionary forecasting method for stock market prediction
    • Araujo R.D. Translation invariant morphological time-lag added evolutionary forecasting method for stock market prediction. Expert Syst. Appl. 2011, 38:2835-2848.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 2835-2848
    • Araujo, R.D.1
  • 16
    • 84885835291 scopus 로고    scopus 로고
    • Fast and simple gradient-based optimization for semi-supervised support vector machines
    • Gieseke F., Airola A., Pahikkala T., Kramer O. Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing 2014, 123:23-32.
    • (2014) Neurocomputing , vol.123 , pp. 23-32
    • Gieseke, F.1    Airola, A.2    Pahikkala, T.3    Kramer, O.4
  • 17
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 1999, 9:293-300.
    • (1999) Neural Process. Lett. , vol.9 , pp. 293-300
    • Suykens, J.A.K.1    Vandewalle, J.2
  • 18
    • 0036825528 scopus 로고    scopus 로고
    • Weighted least squares support vector machines: robustness and sparse approximation
    • Suykens J.A.K., De Brabanter J., Lukas L., Vandewalle J. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 2002, 48:85-105.
    • (2002) Neurocomputing , vol.48 , pp. 85-105
    • Suykens, J.A.K.1    De Brabanter, J.2    Lukas, L.3    Vandewalle, J.4
  • 20
    • 82455199256 scopus 로고    scopus 로고
    • A comparative study of feature extraction methods for the diagnosis of Alzheimer[U+05F3]s disease using the ADNI database
    • Segovia F., Gorriz J.M., Ramirez J., Salas-Gonzalez D., Alvarez I., Lopez M., Chaves R. A comparative study of feature extraction methods for the diagnosis of Alzheimer[U+05F3]s disease using the ADNI database. Neurocomputing 2012, 75:64-71.
    • (2012) Neurocomputing , vol.75 , pp. 64-71
    • Segovia, F.1    Gorriz, J.M.2    Ramirez, J.3    Salas-Gonzalez, D.4    Alvarez, I.5    Lopez, M.6    Chaves, R.7
  • 21
    • 79952534864 scopus 로고    scopus 로고
    • Neural network method for determining embedding dimension of a time series
    • Maus A., Sprott J.C. Neural network method for determining embedding dimension of a time series. Commun. Nonlinear Sci. 2011, 16:3294-3302.
    • (2011) Commun. Nonlinear Sci. , vol.16 , pp. 3294-3302
    • Maus, A.1    Sprott, J.C.2
  • 22
    • 0001874436 scopus 로고    scopus 로고
    • Practical method for determining the minimum embedding dimension of a scalar time series
    • Cao L.Y. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 1997, 110:43-50.
    • (1997) Physica D , vol.110 , pp. 43-50
    • Cao, L.Y.1
  • 23
    • 54049100450 scopus 로고    scopus 로고
    • Regularized least squares fuzzy support vector regression for financial time series forecasting
    • Khemchandani R., Jayadeva, Chandra S. Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst. Appl. 2009, 36:132-138.
    • (2009) Expert Syst. Appl. , vol.36 , pp. 132-138
    • Khemchandani, R.1    Jayadeva2    Chandra, S.3
  • 24
    • 77957308800 scopus 로고    scopus 로고
    • Modeling and prediction of Turkey[U+05F3]s electricity consumption using Support Vector Regression
    • Kavaklioglu K. Modeling and prediction of Turkey[U+05F3]s electricity consumption using Support Vector Regression. Appl. Energy 2011, 88:368-375.
    • (2011) Appl. Energy , vol.88 , pp. 368-375
    • Kavaklioglu, K.1
  • 25
    • 34147111649 scopus 로고    scopus 로고
    • Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
    • An S.J., Liu W.Q., Venkatesh S. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognit. 2007, 40:2154-2162.
    • (2007) Pattern Recognit. , vol.40 , pp. 2154-2162
    • An, S.J.1    Liu, W.Q.2    Venkatesh, S.3
  • 26
    • 79956372247 scopus 로고    scopus 로고
    • A heuristic method for parameter selection in LS-SVM: application to time series prediction
    • Rubio G., Pomares H., Rojas I., Herrera L.J. A heuristic method for parameter selection in LS-SVM: application to time series prediction. Int. J. Forecast. 2011, 27:725-739.
    • (2011) Int. J. Forecast. , vol.27 , pp. 725-739
    • Rubio, G.1    Pomares, H.2    Rojas, I.3    Herrera, L.J.4
  • 27
    • 55749112476 scopus 로고    scopus 로고
    • Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model
    • Hong W.C. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers. Manag. 2009, 50:105-117.
    • (2009) Energy Convers. Manag. , vol.50 , pp. 105-117
    • Hong, W.C.1
  • 28
    • 72049098529 scopus 로고    scopus 로고
    • Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity
    • Yang Z., Gu X.S., Liang X.Y., Ling L.C. Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity. Mater. Des. 2010, 31:1042-1049.
    • (2010) Mater. Des. , vol.31 , pp. 1042-1049
    • Yang, Z.1    Gu, X.S.2    Liang, X.Y.3    Ling, L.C.4
  • 29
    • 18544377981 scopus 로고    scopus 로고
    • Support vector machines with simulated annealing algorithms in electricity load forecasting
    • Pai P.F., Hong W.C. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manag. 2005, 46:2669-2688.
    • (2005) Energy Convers. Manag. , vol.46 , pp. 2669-2688
    • Pai, P.F.1    Hong, W.C.2
  • 30
    • 0001023715 scopus 로고    scopus 로고
    • Application of support vector machines in financial time series forecasting
    • Tay F.E.H., Cao L.J. Application of support vector machines in financial time series forecasting. Omega - Int. J. Manag. Sci. 2001, 29:309-317.
    • (2001) Omega - Int. J. Manag. Sci. , vol.29 , pp. 309-317
    • Tay, F.E.H.1    Cao, L.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.