Structural Engineering and Mechanics

  • 제12권5호
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  • Pages.527-540
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  • 2001
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

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
⟨ 이전 논문 다음 논문 ⟩

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.

키워드

참고문헌

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  1. Development of the Neural Network Algorithm for the Prediction of Column Shortening in High-Rise Buildings vol.15, pp.3, 2012, https://doi.org/10.1260/1369-4332.15.3.509
  2. SUBSTRUCTURE ON-LINE TEST TECHNIQUE USING NON-LINEAR HYSTERESIS MODELLING WITH NEURAL NETWORK vol.69, pp.582, 2004, https://doi.org/10.3130/aijs.69.73_2
  3. SUBSTRUCTURE ON-LINE TEST TECHNIQUE USING REAL-TIME HYSTERESIS MODELING WITH NEURAL NETWORK vol.70, pp.589, 2005, https://doi.org/10.3130/aijs.70.89