Nonlinear prediction of the ionospheric parameter ƒoF2on hourly, daily, and monthly timescales-Reference-Cited by-同舟云学术

Nonlinear prediction of the ionospheric parameter ƒoF2on hourly, daily, and monthly timescales

Published:2000-06-01 Issue:A6 Volume:105 Page:12839-12849
ISSN:0148-0227
Container-title:Journal of Geophysical Research: Space Physics
language:en
Short-container-title:J. Geophys. Res.

Author:

Francis N. M.,Cannon P. S.,Brown A. G.,Broomhead D. S.

Publisher

American Geophysical Union (AGU)

Subject

Paleontology,Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Earth-Surface Processes,Geochemistry and Petrology,Soil Science,Water Science and Technology,Ecology,Aquatic Science,Forestry,Oceanography,Geophysics

Link

http://www.agu.org/journals/ja/v105/iA06/2000JA900005/2000JA900005.pdf

Reference16 articles.

1. The evolution from weak to strong geomagnetic activity: An interpretation in terms of deterministic chaos;Baker;Geophys. Res. Lett.,1990

2. Extracting qualitative dynamics from experimental data;Broomhead;Physica D,1986

3. Multivariable functional interpolation and adaptive networks;Broomhead;Complex Syst.,1988

4. Is there evidence for the existence of nonlinear behavior within the interplanetary solar sector structure?;Brown;J. Geophys. Res.,1999

5. Cander , L. R. S. Stankovic M. Milosavljevic Dynamic ionospheric prediction by neural networks 1997

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Forecasting Model of Ionospheric foF2 Using the LSTM Network Based on ICEEMDAN Decomposition;IEEE Transactions on Geoscience and Remote Sensing;2023

2. A novel flexible model to calibrate single-layer height for ionospheric modelling using neural network model;2022-08-24

3. An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network;Remote Sensing;2022-05-19

4. A novel hybrid Machine learning model to forecast ionospheric TEC over Low-latitude GNSS stations;Advances in Space Research;2022-02

5. Machine learning regression models for prediction of multiple ionospheric parameters;Advances in Space Research;2022-02