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Neural-network-based Prediction Techniques for Single Station Modeling and Regional Mapping of the FoF2 and M(3000)F2 Ionospheric Characteristics : Volume 9, Issue 5/6 (30/11/-0001)

By Xenos, T. D.

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Book Id: WPLBN0004020049
Format Type: PDF Article :
File Size: Pages 10
Reproduction Date: 2015

Title: Neural-network-based Prediction Techniques for Single Station Modeling and Regional Mapping of the FoF2 and M(3000)F2 Ionospheric Characteristics : Volume 9, Issue 5/6 (30/11/-0001)  
Author: Xenos, T. D.
Volume: Vol. 9, Issue 5/6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
-0001
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Xenos, T. D. (-0001). Neural-network-based Prediction Techniques for Single Station Modeling and Regional Mapping of the FoF2 and M(3000)F2 Ionospheric Characteristics : Volume 9, Issue 5/6 (30/11/-0001). Retrieved from http://hawaiilibrary.net/


Description
Description: Aristotelian University of Thessaloniki, Dept of Electrical and Computers Eng., 54006 Thessaloniki, Greece. In this work, Neural-Network-based single-station hourly daily foF2 and M(3000)F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily values from the period 1964- 1988 for training the neural networks and from the period 1989-1994 for checking the prediction accuracy. Two types of models are presented for the F2-layer critical frequency prediction and two for the propagation factor M(3000)F2. The first foF2 model employs the E-layer local noon calculated daily critical frequency (foE12) and the local noon F2- layer critical frequency of the previous day. The second foF2 model, which introduces a new regional mapping technique, employs the Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12), and the previous day F2-layer critical frequency measured at Juliusruh at noon. The first M(3000)F2 model employs the E-layer local noon calculated daily critical frequency (foE12), its ± 3 h deviations and the local noon cosine of the solar zenith angle (cos c12). The second model, which introduces a new M(3000)F2 mapping technique, employs Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE12), and the previous day F2-layer critical frequency measured at Juliusruh at noon.

Summary
Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000)F2 ionospheric characteristics

 

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