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Neural Network Development for the Forecasting of Upper Atmosphere Parameter Distributions

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dc.contributor.author Zhou, Qihou en_US
dc.contributor.author Morton, Yu en_US
dc.contributor.author Jeffrey, Martin en_US
dc.date.accessioned 2008-12-15T22:09:24Z en_US
dc.date.accessioned 2013-07-10T15:07:00Z
dc.date.available 2008-12-15T22:09:24Z en_US
dc.date.available 2013-07-10T15:07:00Z
dc.date.created 2003-12 en_US
dc.date.issued 2008-12-15T22:09:24Z en_US
dc.identifier.uri
dc.identifier.uri http://hdl.handle.net/2374.MIA/276 en_US
dc.description This paper presents a neural network modeling approach to forecast electron concentration distributions in the 150â 600 km altitude range above Arecibo, Puerto Rico. The neural network was trained using incoherent scatter radar data collected at the Arecibo Observatory during the past two decades, as well as the Kp geomagnetic index provided by the National Space Science Data Center. The data set covered nearly two solar cycles, allowing the neural network to model daily, seasonal, and solar cycle variations of upper atmospheric parameter distributions. Two types of neural network architectures, feedforward and Elman recurrent, are used in this study. Topics discussed include the network design, training strategy, data analysis, as well as preliminary testing results of the networks on electron concentration distributions. en
dc.language.iso en en
dc.title Neural Network Development for the Forecasting of Upper Atmosphere Parameter Distributions en
dc.type Text en_US
dc.type.genre Article en_US


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