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 Posted: Sun 3:19, 08 May 2011 Post subject: ed hardy danmark Dynamic recurrent neural networks |
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,[link widoczny dla zalogowanych]
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,[link widoczny dla zalogowanych], using the input branch recursive processing,[link widoczny dla zalogowanych], 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,[link widoczny dla zalogowanych], 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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