scholarly journals Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1328-1345 ◽  
Author(s):  
Jean Claude Uwamahoro ◽  
Nigussie M. Giday ◽  
John Bosco Habarulema ◽  
Zama T. Katamzi‐Joseph ◽  
Gopi Krishna Seemala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Total Electron Content ◽  
Electron Content ◽  
African Region ◽  
Total Electron ◽  
Ionospheric Tomography ◽  
Artificial Neural

scholarly journals Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

Space Weather ◽  
2020 ◽  
Vol 18 (9) ◽  
Author(s):  
Daniel Okoh ◽  
John Bosco Habarulema ◽  
Babatunde Rabiu ◽  
Gopi Seemala ◽  
Joshua Benjamin Wisdom ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Total Electron Content ◽  
Electron Content ◽  
Total Electron ◽  
Ionospheric Total Electron Content ◽  
Time Modeling ◽  
Artificial Neural

scholarly journals Efficiency of artificial neural networks in map of total electron content over Iran

2015 ◽  
Vol 51 (3) ◽  
pp. 541-555 ◽  
Author(s):  
Mir Reza Ghaffari Razin ◽  
Behzad Voosoghi ◽  
Ali Mohammadzadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Total Electron Content ◽  
Electron Content ◽  
Total Electron ◽  
Artificial Neural

scholarly journals Artificial neural network applications in ionospheric studies

1998 ◽  
Vol 41 (5-6) ◽  
Author(s):  
L. R. Cander
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Electron Content ◽  
Total Electron ◽  
Ionospheric Physics ◽  
Modern Development ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Electron Density Profiles

The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC). Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

Kanbansetting through artificial intelligence: a comparative study of artificial neural networks and decision trees

2000 ◽  
Vol 11 (4) ◽  
pp. 239-246 ◽  
Author(s):  
Ina S. Markham ◽  
Richard G. Mathieu ◽  
Barry A. Wray
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Decision Trees ◽  
Artificial Neural

Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers

2020 ◽  
pp. 302-318 ◽  
Author(s):  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Nilesh Patel ◽  
Saurabh Gupta ◽  
Gazal Varshney
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
State Of The Art ◽  
Artificial Neural
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