Computational Structural Engineering (전산구조공학)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
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Applications of Probabilistic Neural Networks in Structural Engineering

구조공학에서의 확률신경망이론의 응응

  • 김두기 (군산대학교 토목환경공학부) ;
  • 이종재 (한국과학기술원 스마트 사회기반시설연구센터) ;
  • 장성규 (군산대학교 토목환경공학부) ;
  • 장상길 (군산대학교 토목환경공학부)
  • Published : 2006.12.21
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References

  1. Aoki, T., Ceravolo, R., De Stefano, A.,Genovese, C. and Sabia, D. (2002) Seismic vulnerability assessment of chemical plants through probabilistic neural networks, Reliability engineering & system safety, 77(3). pp. 263-268 https://doi.org/10.1016/S0951-8320(02)00059-5
  2. Cacoullos, T. (1966) Estimation of a multivariate density, Annals of the Institute of Statistical Mathematics (Tokyo), 18(2), pp. 179-189 https://doi.org/10.1007/BF02869528
  3. Chtioui, Y., Bertrand, D., Devaux, M.F. and Barba, D. (1997) Comparison of multilayer perceptron and probabilistic neural networks in artificial vision. Application to the discrimination of seeds, Journal of chemometrics, 11(2), pp.111-129 https://doi.org/10.1002/(SICI)1099-128X(199703)11:2<111::AID-CEM455>3.0.CO;2-V
  4. Goh, A. T. C. (2002) Probabilistic neural network for evaluating seismic liquefaction potential, Canadian geotechnical journal: Revuecanadienne de geotechnique, 39(1), pp. 219-232 https://doi.org/10.1139/t01-073
  5. Haykin, S. (1994) Neural networks-a comprehensive foundation, New York Macmillan
  6. Holmes, E., Nicholson, J. K., and Tranter, G. (2001) Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks, Chemical research in toxicology, 14(2), pp. 182-191 https://doi.org/10.1021/tx000158x
  7. Hudson, R.Y. (1958) Design of Quarry Stone Cover Layer For Rubble Mound Breakwaters. Research Report (2-2). Waterways Experiment Station, Coastal Engineering Research Centre, Vicksburg, Miss
  8. Lee, J.J. and Yun, C.B. (2006) Two-Step Approaches for Effective Bridge Health Monitoring, Structural Engineering and Mechanics: 23(1), pp. 75-95 https://doi.org/10.2208/jsceseee.23.75s
  9. Lin, S. H., Kung, S. Y. and Lin, L. J. (1997) Face recognition/detection by probabilistic decisionbased neural network, IEEE transactions on neural networks, 8(1), pp. 114-132 https://doi.org/10.1109/72.554196
  10. Ni, Y.Q., Zhou, X.T., Ko, J.M. and Wang, B.S. (2000) Vibration-based damage localization in Ting Kau Bridge using probabilistic neural network, Advances in Structural Dynamics, J.M. Ko and Y.L. Xu (eds.), Elsevier Science Ltd., Oxford, UK, 2, pp. 1069-1076
  11. Parzen, E. (1962) On estimation of a probability density function and mode, Annals of Mathematical Statistics, 33, pp. 1065-1076 https://doi.org/10.1214/aoms/1177704472
  12. Raghu, P.P. and Yegnanarayana, B. (1998) Supervised texture classification using a probabilistic neural network and constraint satisfaction model, IEEE transactions on neural networks, 9(3), pp. 516-522 https://doi.org/10.1109/72.668893
  13. Rumelhart, D. E., McClelland, J. L. and the PDP Research Group. (1986) Parallel distributed processing, The MlT Press, Cambridge, MA, 1
  14. Sinha, S. K. and Pandey, M. D. (2002) Probabilistic neural network for reliability assessment of oil and gas pipelines, Computer-aided civil and infrastructure engineering, 17(5), pp. 320-329 https://doi.org/10.1111/1467-8667.00279
  15. Touretzky, D.S., Thibadeau, R.H. and Romero, R.D. (1997) Optical Chinese character recognition using probabilistic neural networks, Pattern recognition, 30(8), pp. 1279-1292 https://doi.org/10.1016/S0031-3203(96)00166-5
  16. van der Meer, J.W. (1988) Deterministic and probabilistic design of breakwater armor layers, J. Wtrwy. Port Coast., Ocean Eng., 114(1), pp. 66-80 https://doi.org/10.1061/(ASCE)0733-950X(1988)114:1(66)
  17. Wang, Y., Adali, T., Kung, S. Y. and Szabo, Z. (1998) Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach, IEEE transactions on image processing, 7(8), pp. 1165-1181 https://doi.org/10.1109/83.704309
  18. Willmott, C.J. (1981) On the validation of models, Phys. Geogr., 2(2), pp. 184-194
  19. Yang, Z. R., Platt, M. B. and Platt, H. D. (1999) Probabilistic neural networks in bankruptcy prediction, Journal of business research, 44(2), pp. 67-74 https://doi.org/10.1016/S0148-2963(97)00242-7
  20. Zaknich, A. (1990) Introduction to the modified probabilistic neural network for general signal processing applications,IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, 46(7), pp. 1980-1990 https://doi.org/10.1109/78.700969
  21. 김두기, 김동현, 장성규, 장상길 (2006) 확률신경망을 이용한 방파제 피복재 설계, 한국해양공학회지 논문집, 20(5)
  22. 김두기, 이종재, 장성규 (2004) 콘크리트 압축강도 추정을 위한 확률 신경망, 한국구조물진단학회지 논문집, 8(2), p. 159-167
  23. 김두기, 이종재, 장성규, 최인정 (2007) 지진하중을 받는 구조물의 능동제어를 위한 확률신경망 이론, 한국구조물진단학회지 논문집, 11(1)
  24. 이창용, 김용석, 신현석, 김중훈 (2000) 확률적 신경망을 이용한 상수도관 노후도 추정에 관한 연구, 대한토목학회 논문집, 20(2-B), p. 197-210
  25. 조효남, 강경구, 이성칠, 허춘근 (2002) 확률신경망에 기초한 교량구조물의 손상평가, 한국구조물진단학회논문집, 6(4), p. 169-179