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 Wysłany: Nie 3:10, 08 Maj 2011 Temat postu: Franklin Marshall pas cher Dynamic recurrent neura |
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,Franklin Marshall pas cher
Dynamic recurrent neural networks based on semi-active control of structural response prediction
Gou external excitation of the structure containing the response have different effects,Abercrombie Fitch uk, using the input branch recursive processing,tory burch outlet, thus greatly improving the dynamic recurrent neural network r learning and training efficiency. Application proverb model nonlinear structure in the linear structure and variable damping control and incentive structures under external loads are simulated responses,Tory Burch shoes, indicating that the proposed recurrent neural network can achieve higher prediction accuracy. The neural network model for the use of semi-active variable damper control of the foundation structure. References 1 Wang Tinan. Intelligent control system. Hunan: Hunan University Press 19962Ghaboussij-Joghata [e. Activecontro [of5tructtlresusingneuralnetworks. JEngrg. MechASCE. I2 (4) :555-5673Bani-HaniKtGhaboussij. Nonlinearsrrtlctura] controlusingneura [networksj. Engrg. MechASCEl24 (3) = 3193274ChassiakosAG. MasriSF. Modelingunknownstrtlctttra [systemsthroughtheuseofneuralhe1worksj. EarthquakeEngrgandStrue1. Dyn1996; 25:117128 j Sunzuo Yu. Semi-active structural control variable damper: [Dissertation:. Harbin: Harbin Institute of Architecture University of 19986BabeTTTWenYK. Randomvibrationofhystereticdegradingsystems. JEngrg. MechAS ('E.19811071069】 078ResponsePredictionofStructurewithVariableDampingCoefficientsBasedonDynamicRecurrentNeuralNetworkSunZuo> u (DepartmentofCivilEngineering.TianjinUniversityTianjint300072) AbstractAramoseinputandmulti-ou | putdynamicrecurrentneuralnetworkmodelRDRNNispresentedinthepaperTheralllOSeinputofRDRNNdealswiththedillerentresponsea ~ eetion0feachkind. FinputInthehidden [ayer.eachneuralunitrecurrentitselfdynamically.ThelearningandtrainingprocedureoftheRDRNNisvervefileien 【foritsranloseinputandrecurrentframe.Simurationson1lnearandn0nlinearstructuresdemonstratethatRDRNisveryeffectixeonpredictingtheresponseoiastructureSUbjecttOsemi-activecontrolandexterna [exaltation.RDRNNishighlyvaluableforsemi-activestructuralcontrol10astructureequippedwithvariabledampersKeywordsneuraJnetwork; responseprediction: sem a activecontrol; variabledamper On Sun Zuoyu men. Associate Professor of .1963 in September of Health Tel: (046735 () 7047g: E-mallnzuoyu @ yahoo ∞
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