메뉴 건너뛰기




Volumn 18, Issue 2, 2004, Pages 105-114

Data division for developing neural networks applied to geotechnical engineering

Author keywords

Data processing; Fuzzy sets; Geotechnical engineering; Maps; Neural networks; Settlement

Indexed keywords

CROSS-VALIDATION TECHNIQUE; DATA DIVISION; GRANULAR SOILS; SETTLEMENT;

EID: 16444364474     PISSN: 08873801     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)0887-3801(2004)18:2(105)     Document Type: Article
Times cited : (316)

References (25)
  • 1
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. I: Preliminary concepts
    • ASCE Task Committee of Artificial Neural Networks in Hydrology. (2000). "Artificial neural networks in hydrology. I: Preliminary concepts." J. Hydrologic Eng., 5(2), 115-123.
    • (2000) J. Hydrologic Eng. , vol.5 , Issue.2 , pp. 115-123
  • 2
    • 0036221122 scopus 로고    scopus 로고
    • Optimal division of data for neural network models in water resources applications
    • Bowden, G. J., Maier, H. R., and Dandy, G. C. (2002). "Optimal division of data for neural network models in water resources applications." Water Resour. Res., 38(2), 2.1-2.11.
    • (2002) Water Resour. Res. , vol.38 , Issue.2
    • Bowden, G.J.1    Maier, H.R.2    Dandy, G.C.3
  • 3
    • 0031851620 scopus 로고    scopus 로고
    • Feed-forward artificial neural network model for forecasting rainfall runoff
    • Braddock, R. D., Kremmer, M. L., and Sanzogni, L. (1998). "Feed-forward artificial neural network model for forecasting rainfall runoff." Environmetrics, 9, 419-432.
    • (1998) Environmetrics , vol.9 , pp. 419-432
    • Braddock, R.D.1    Kremmer, M.L.2    Sanzogni, L.3
  • 4
    • 0022205468 scopus 로고
    • Settlement of foundations on sand and gravel
    • Burland, J. B., and Burbidge, M. C. (1985). "Settlement of foundations on sand and gravel." Proc., Inst. Civ. Eng., 78(1), 1325-1381.
    • (1985) Proc., Inst. Civ. Eng. , vol.78 , Issue.1 , pp. 1325-1381
    • Burland, J.B.1    Burbidge, M.C.2
  • 5
    • 0005380346 scopus 로고
    • Neural networks primer. Part III
    • Caudill, M. (1988). "Neural networks primer. Part III." AI Expert, 3(6), 53-59.
    • (1988) AI Expert , vol.3 , Issue.6 , pp. 53-59
    • Caudill, M.1
  • 7
    • 0035673991 scopus 로고    scopus 로고
    • A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination
    • Giraudel, J. L., and Lek, S. (2001). "A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination." Ecol. Modell., 146, 329-339.
    • (2001) Ecol. Modell. , vol.146 , pp. 329-339
    • Giraudel, J.L.1    Lek, S.2
  • 8
    • 0027627965 scopus 로고
    • Working with neural networks
    • Hammerstrom, D. (1993). "Working with neural networks." IEEE Spectrum, 30(7), 46-53.
    • (1993) IEEE Spectrum , vol.30 , Issue.7 , pp. 46-53
    • Hammerstrom, D.1
  • 9
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik, K., Stinchcombe, M., and White, H. (1989). "Multilayer feedforward networks are universal approximators." Neural Networks, 2, 359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 11
    • 0034578871 scopus 로고    scopus 로고
    • Modeling of the river flowrate: The influence of the training set selection
    • Kocjancic, R., and Zupan, J. (2000). "Modeling of the river flowrate: the influence of the training set selection." Chemom. Intell. Lab. Syst., 54, 21-34.
    • (2000) Chemom. Intell. Lab. Syst. , vol.54 , pp. 21-34
    • Kocjancic, R.1    Zupan, J.2
  • 14
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications
    • Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications." Environ. Modell. Softw., 15, 101-124.
    • (2000) Environ. Modell. Softw. , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 16
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural networks as rainfall-runoff models
    • Minns, A. W., and Hall, M. J. (1996). "Artificial neural networks as rainfall-runoff models." Hydrol. Sci. J., 41(3), 399-417.
    • (1996) Hydrol. Sci. J. , vol.41 , Issue.3 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 17
    • 0242644816 scopus 로고    scopus 로고
    • Southampton, U.K.
    • Neusciences Corp. (2000). Neuframe version 4.0, Southampton, U.K.
    • (2000) Neuframe Version 4.0
  • 18
    • 0000646059 scopus 로고
    • Learning internal representation by error propagation
    • D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, Mass.
    • Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). "Learning internal representation by error propagation." Parallel distributed processing, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, Mass.
    • (1986) Parallel Distributed Processing
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 19
    • 84930208606 scopus 로고    scopus 로고
    • Artificial neural network applications in geotechnical engineering
    • Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artificial neural network applications in geotechnical engineering." Australian Geomech., 36(1), 49-62.
    • (2001) Australian Geomech. , vol.36 , Issue.1 , pp. 49-62
    • Shahin, M.A.1    Jaksa, M.B.2    Maier, H.R.3
  • 20
    • 0036712318 scopus 로고    scopus 로고
    • Predicting settlement of shallow foundations using neural networks
    • Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2002). "Predicting settlement of shallow foundations using neural networks." J. Geotech. Geoenviron. Eng., 128(9), 785-793.
    • (2002) J. Geotech. Geoenviron. Eng. , vol.128 , Issue.9 , pp. 785-793
    • Shahin, M.A.1    Maier, H.R.2    Jaksa, M.B.3
  • 21
    • 1842545602 scopus 로고    scopus 로고
    • Clustering technique for evaluating and validating neural network performance
    • Shi, J. J. (2002). "Clustering technique for evaluating and validating neural network performance." J. Comput. Civ. Eng., 16(2), 152-155.
    • (2002) J. Comput. Civ. Eng. , vol.16 , Issue.2 , pp. 152-155
    • Shi, J.J.1
  • 23
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions
    • Stone, M. (1974). "Cross-validatory choice and assessment of statistical predictions." J. R. Stat. Soc., B-36, 111-147.
    • (1974) J. R. Stat. Soc. , vol.B-36 , pp. 111-147
    • Stone, M.1
  • 24
    • 0033167344 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using artificial neural networks
    • Tokar, A. S., and Johnson, P. A. (1999). "Rainfall-runoff modeling using artificial neural networks." J. Hydrologic Eng., 4(3), 232-239.
    • (1999) J. Hydrologic Eng. , vol.4 , Issue.3 , pp. 232-239
    • Tokar, A.S.1    Johnson, P.A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.