 Posted: Sun 3:19, 08 May 2011 Post subject: ed hardy danmark Dynamic recurrent neural networks |
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| ,ed hardy danmark
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,Ed Hardy mexico, using the input branch recursive processing,Franklin and Marshall uk, 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 UK, 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 ∞ 相关的主题文章： Oakley juliet Large bags at home and abroad and eq jordan shoes Fast printing industry domestic and i abercrombie en fitch Modify the characteristics of |
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