DOI QR코드

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Whole learning algorithm of the neural network for modeling nonlinear and dynamic behavior of RC members

  • Satoh, Kayo (Institute of Industrial Science, The University of Tokyo) ;
  • Yoshikawa, Nobuhiro (Institute of Industrial Science, The University of Tokyo) ;
  • Nakano, Yoshiaki (Institute of Industrial Science, The University of Tokyo) ;
  • Yang, Won-Jik (Institute of Industrial Science, The University of Tokyo)
  • 발행 : 2001.11.25

초록

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

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참고문헌

  1. Ghaboussi, J., Garrett, Jr., J.H., and Wu, X. (1991), "Knowledge-based modeling of material behavior with neural networks", J. Eng. Mech., ASCE, 117(1), 132-153. https://doi.org/10.1061/(ASCE)0733-9399(1991)117:1(132)
  2. Hangai, Y., and Kawaguchi, K. (1991), Shape Analysis (in Japanese), Baifukan, Tokyo.
  3. Hertz, J., Krogh, A., and Palmer, R.G. (1991), Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, Redwood City.
  4. Magoulas, G.D., Vrahatis, M.N., and Androulakis, G.S. (1999), "Improving the convergence of the backpropagation algorithm using learning rate adaptation"', Neural Computation, 11(7), 1769-1796. https://doi.org/10.1162/089976699300016223
  5. Okada, T., Kumazawa, F., and Nishida, T. (1988), "Earthquake response of reinforced concrete weak-model structures due to December 17, 1987 earthquake", Institute of Industrial Science, The University of Tokyo, Bull. ERS, (21), 67-78.
  6. Pal, C., Kayaba, N., Morishita, S., and Hagiwara, I. (1994), "New learning method of neural network by pseudoinverse technique (in Japanese)", Trans. JSME C, 60(573), 1699-1704. https://doi.org/10.1299/kikaic.60.1699
  7. Plaut, D., Nowlan, S., and Hinton G.E. (1986), "Experiments on learning by back propagation", Technical Report CMU-CS-86-126, Department of Computer Science, Carnegie Mellon University, Pittsburgh.
  8. Ramberg, W., and Osgood, W.R. (1943), "Description of stress-strain curves by three parameters", NACA Technical Note, 902, 1-22.
  9. Rao, C.R., and Mitra, S.K. (1971), Generalized Inverse of Matrices and Its Applications, John Wiley & Sons, Inc., New York.
  10. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986), "Learning representations by back-propagating errors", Nature, 323(9), 533-536. https://doi.org/10.1038/323533a0
  11. Rumelhart, D.E., McClelland, J.L., and the PDP Research Group (1986), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, MIT Press, Cambridge.
  12. Weir, M.K. (1991), "A method for self-determination of adaptive learning rates in back propagation", Neural Networks, 4(3), 371-379. https://doi.org/10.1016/0893-6080(91)90073-E
  13. Yamamoto, K. (1992), "Modeling of hysteretic behavior with neural networks and its application to non-linear dynamic analysis (in Japanese)", J. Struct. Eng., JSCE, 38, 85-94.

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