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Hydrol. Earth Syst. Sci., 17, 253–267, 2013 www.hydrol-earth-syst-sci.netdoi:hess © Author(sCC Attribution 3.0 License. Hydrology and
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Document Date: 2014-12-04 03:21:36

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City

Sacramento / /

Company

BP / ESN / /

Country

United States / Brazil / /

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IndustryTerm

few applications / radial basis function network / recurrent artificial neural networks / possible solution / recurrent networks / echo state network / layered echo state networks / state networks / function approximation applications / neural networks / echo state networks / equivalent feedforward network / recurrent network / optimisation algorithms / gradient algorithm / computing / feedforward networks / online mode / artificial neural networks / stateof-the-art recurrent networks / recurrent training algorithms / possible applications / Reservoir computing / soil moisture accounting model / dynamical systems / /

NaturalFeature

Great Lakes / /

OperatingSystem

Fork / /

Organization

European Geosciences Union / National Weather Service / /

Person

LIN FF / Clay Clay / Kamerlingh Onneslaan / /

Position

LESN LESN−L PM / B7 B8 B9 B10 PM / Prime Minister / Network structure+ Model Architecture Inputs PM / Rt / forward network connections / /

Product

Pentax K-x Digital Camera / /

ProgrammingLanguage

RC / R / K / /

ProvinceOrState

New Jersey / /

Region

Eastern United States / /

Technology

t -1 / BPTT algorithms / neural network / optimisation algorithms / LM algorithm / two recurrent training algorithms / machine learning / simulation / flash / BPTT training algorithm / gradient algorithm / /

URL

www.hydrol-earth-syst-sci.net/17/253/2013 / /

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