Diurnal specification of the ionosphericf0F2parameter using a support vector machine
Author:
Affiliation:
1. School of Science; Xidian University; Xi'an; China
2. National Key Laboratory of Electromagnetic Environment; China Research Institute of Radiowave Propagation; Qingdao; China
Publisher
American Geophysical Union (AGU)
Subject
Electrical and Electronic Engineering,General Earth and Planetary Sciences,Condensed Matter Physics
Link
http://www.agu.org/journals/rs/rs1005/2010RS004393/2010RS004393.pdf
Reference22 articles.
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2. Ionospheric forecasting technique by artificial neural network;Cander;Electron. Lett.,1998
3. A short-term f0F2 forecasting method based on neural network techniques;Chen;Chin. J. Radio Sci.,2008
4. Low latitude storm time ionospheric electrodynamics;Fejer;J. Atmos. Sol. Terr. Phys.,2002
5. Nonlinear prediction of the ionospheric parameter on hourly, daily, and monthly timescales;Francis;J. Geophys. Res.,2000
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