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Dynamic analysis of nonlinear frames by prandtl neural networks / Abdolreza Joghataie in Journal of engineering mechanics, Vol. 134 n°11 (Novembre 2008) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 134 n°11 (Novembre 2008) . - pp.961–969. Titre : Dynamic analysis of nonlinear frames by prandtl neural networks Type de document : texte imprimé Auteurs : Abdolreza Joghataie, Auteur ; Mojtaba Farrokh, Auteur Année de publication : 2008 Article en page(s) : pp.961–969. Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Neural  networks  Algorithms  Nonlinear  analysis  Hysteresis  Frames  Ground  motion  Dynamic  analysis Résumé : A new type of activation function, based on the use of the Prandtl–Ishlinskii operator, has been developed and used in the feed forward neural networks in order to improve their capabilities in learning to identify and analyze nonlinear structures subject to dynamic loading. The genetic algorithm has been used in its training. The neural network, which is referred to as the Prandtl neural network here, has been trained and used in the analysis of two shear frames, a single degree of freedom (SDOF) and a 3DOF, both subjected to earthquake excitations. To assess the capabilities of the Prandtl neural network under ideal situations, the data on the response of the frames have been obtained through the integration of their governing nonlinear equations of motion. The training has been based on the white noise while the strong earthquakes of 200% El Centro in 1940 and Gilroy have been used for testing. Results have shown the high precision of the Prandtl neural network in solving highly hysteretic problems. The issue is important for two main applications in structural dynamics and control: (1) analysis of highly nonlinear structures where it is desired to train a neural network to directly learn the behavior of a structure from experimental data; and (2) intelligent active control of structures where neural network emulators are designed to provide as precise predictions about the future response of the structures as possible, in order to be used in the determination of the required control forces. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282008%29134%3A11%289 [...] [article] Dynamic analysis of nonlinear frames by prandtl neural networks [texte imprimé] / Abdolreza Joghataie, Auteur ; Mojtaba Farrokh, Auteur . - 2008 . - pp.961–969. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 134 n°11 (Novembre 2008) . - pp.961–969. Mots-clés : Neural  networks  Algorithms  Nonlinear  analysis  Hysteresis  Frames  Ground  motion  Dynamic  analysis Résumé : A new type of activation function, based on the use of the Prandtl–Ishlinskii operator, has been developed and used in the feed forward neural networks in order to improve their capabilities in learning to identify and analyze nonlinear structures subject to dynamic loading. The genetic algorithm has been used in its training. The neural network, which is referred to as the Prandtl neural network here, has been trained and used in the analysis of two shear frames, a single degree of freedom (SDOF) and a 3DOF, both subjected to earthquake excitations. To assess the capabilities of the Prandtl neural network under ideal situations, the data on the response of the frames have been obtained through the integration of their governing nonlinear equations of motion. The training has been based on the white noise while the strong earthquakes of 200% El Centro in 1940 and Gilroy have been used for testing. Results have shown the high precision of the Prandtl neural network in solving highly hysteretic problems. The issue is important for two main applications in structural dynamics and control: (1) analysis of highly nonlinear structures where it is desired to train a neural network to directly learn the behavior of a structure from experimental data; and (2) intelligent active control of structures where neural network emulators are designed to provide as precise predictions about the future response of the structures as possible, in order to be used in the determination of the required control forces. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282008%29134%3A11%289 [...]