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

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

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

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

構造及び地震工学

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

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月 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月 4 2002 → 9月 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
City	Prague
Period	9/4/02 → 9/6/02

!!!All Science Journal Classification (ASJC) codes

工学（全般）

他のファイルとリンク

引用スタイル

Mazda, T., Otsuka, H., Yabuki, W., & Tsuruta, M. (2002). A study on generalized neural network system for recognizing nonlinear behaviour of structures. In B. H. V. Topping, Z. Bittnar, B. H. V. Topping, & Z. Bittnar (Eds.), Proceedings of the Third International Conference on Engineering Computational Technology (pp. 209-210). (Proceedings of the Third International Conference on Engineering Computational Technology).

A study on generalized neural network system for recognizing nonlinear behaviour of structures. / Mazda, T.; Otsuka, H.; Yabuki, W. その他.
Proceedings of the Third International Conference on Engineering Computational Technology. ed. / B.H.V. Topping; Z. Bittnar; B.H.V. Topping; Z. Bittnar. 2002. p. 209-210 (Proceedings of the Third International Conference on Engineering Computational Technology).

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

Mazda, T, Otsuka, H, Yabuki, W & Tsuruta, M 2002, A study on generalized neural network system for recognizing nonlinear behaviour of structures. in BHV Topping, Z Bittnar, BHV Topping & Z Bittnar (eds), Proceedings of the Third International Conference on Engineering Computational Technology. Proceedings of the Third International Conference on Engineering Computational Technology, pp. 209-210, Proceedings of the Third International Conference on Engineering Computational Technology, Prague, チェコ共和国, 9/4/02.

Mazda T, Otsuka H, Yabuki W, Tsuruta M. A study on generalized neural network system for recognizing nonlinear behaviour of structures. In Topping BHV, Bittnar Z, Topping BHV, Bittnar Z, editors, Proceedings of the Third International Conference on Engineering Computational Technology. 2002. p. 209-210. (Proceedings of the Third International Conference on Engineering Computational Technology).

Mazda, T. ; Otsuka, H. ; Yabuki, W. その他. / A study on generalized neural network system for recognizing nonlinear behaviour of structures. Proceedings of the Third International Conference on Engineering Computational Technology. editor / B.H.V. Topping ; Z. Bittnar ; B.H.V. Topping ; Z. Bittnar. 2002. pp. 209-210 (Proceedings of the Third International Conference on Engineering Computational Technology).

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title = "A study on generalized neural network system for recognizing nonlinear behaviour of structures",

abstract = "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.",

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AB - 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.

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

抄録

出版物シリーズ

その他

!!!All Science Journal Classification (ASJC) codes

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