A study on generalized neural network system for recognizing nonlinear behaviour of structures

T. Mazda, H. Otsuka, W. Yabuki, M. Tsuruta

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

Multiple layered neural network to simulate the non-linear hysteretic behavior like Ramberg-Osgood model, modified bilinear model and Takeda model is described. Based on the pattern recognition ability of neural network, non-linear hysteretic behavior is modeled by the network directly without replacing it with a mathematical model. Thus, the effectiveness and applicability of the network in numerical analysis are evaluated. It is found that neural network transmits the signals from the input layer to the output layer by way of the hidden layers when the input signals is received, and output layer by way o the hidden layers when the input signals is received, and output the output signals finally.

本文言語英語
ホスト出版物のタイトルProceedings of the Third International Conference on Engineering Computational Technology
編集者B.H.V. Topping, Z. Bittnar, B.H.V. Topping, Z. Bittnar
ページ209-210
ページ数2
出版ステータス出版済み - 12 1 2002
イベントProceedings of the Third International Conference on Engineering Computational Technology - Prague, チェコ共和国
継続期間: 9 4 20029 6 2002

出版物シリーズ

名前Proceedings of the Third International Conference on Engineering Computational Technology

その他

その他Proceedings of the Third International Conference on Engineering Computational Technology
国/地域チェコ共和国
CityPrague
Period9/4/029/6/02

All Science Journal Classification (ASJC) codes

  • 工学(全般)

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