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Volumn 14, Issue 1, 2010, Pages 199-206

Epoch determination for neural network by self-organized map (SOM)

Author keywords

Artificial neural network; Kohonen network; Self organizing map; Supervised learning; Unsupervised learning

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATA SET; LEARNING; NUMERICAL MODEL; SELF ORGANIZATION; STATISTICAL ANALYSIS; TRAINING;

EID: 77952881139     PISSN: 14200597     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10596-009-9143-0     Document Type: Article
Times cited : (43)

References (15)
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    • Ghaboussi, J.1    Garrett Jr., J.H.2    Wu, X.3
  • 2
    • 0026711890 scopus 로고
    • Neural network modeling of the mechanical behavior of sand
    • N. Y.: ASCE
    • Ellis, G. W., Yao, C., Zhao, R.: Neural network modeling of the mechanical behavior of sand. In: Proc., 9th Conf., ASCE Eng. Mech., pp. 421-424. ASCE, N. Y. (1992).
    • (1992) Proc., 9th Conf., ASCE Eng. Mech. , pp. 421-424
    • Ellis, G.W.1    Yao, C.2    Zhao, R.3
  • 12
    • 0035386660 scopus 로고    scopus 로고
    • Application of artificial neural networks to fracture analysis at the Aspo HRL, Sweden: Fracture sets classification
    • Sirat, M., Tablot, C. J.: Application of artificial neural networks to fracture analysis at the Aspo HRL, Sweden: fracture sets classification. Int. J. Rock Mech. Min. Sci. 38, 621-639 (2001).
    • (2001) Int. J. Rock Mech. Min. Sci. , vol.38 , pp. 621-639
    • Sirat, M.1    Tablot, C.J.2
  • 13
    • 0034578871 scopus 로고    scopus 로고
    • Modeling of river flow rate: The influence of the training set selection
    • Kocjancic, R., Zupan, J.: Modeling of river flow rate: the influence of the training set selection. Chemom. Intell. Lab. Syst. 54, 21-34 (2000).
    • (2000) Chemom. Intell. Lab. Syst. , vol.54 , pp. 21-34
    • Kocjancic, R.1    Zupan, J.2
  • 14
    • 0036221122 scopus 로고    scopus 로고
    • Optimal division of data for neural network models in water resources applications
    • Bowden, G. J., Maier, H. R., Dandy, G. C.: Optimal division of data for neural network models in water resources applications. Water Resour. Res. 38(2), 2. 1-2. 11 (2002).
    • (2002) Water Resour. Res. , vol.38 , Issue.2 , pp. 1-11
    • Bowden, G.J.1    Maier, H.R.2    Dandy, G.C.3
  • 15
    • 16444364474 scopus 로고    scopus 로고
    • Data division for developing neural networks applied to geotechnical engineering
    • Shahin, M. A., Maier, H. R., Jaska, M. B.: Data division for developing neural networks applied to geotechnical engineering. J. Comput. Civ. Eng. ASCE 18(2), 105-114 (2004).
    • (2004) J. Comput. Civ. Eng. ASCE , vol.18 , Issue.2 , pp. 105-114
    • Shahin, M.A.1    Maier, H.R.2    Jaska, M.B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.