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A Prediction Model of Ionospheric f o F 2 Based on Extreme Learning Machine
Author(s) -
Bai Hongmei,
Fu Haipeng,
Wang Jian,
Ma Kaixue,
Wu Taosuo,
Ma Jianguo
Publication year - 2018
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2018rs006622
Subject(s) - extreme learning machine , ionosphere , artificial neural network , earth's magnetic field , radar , international reference ionosphere , algorithm , computer science , feedforward neural network , artificial intelligence , physics , telecommunications , total electron content , geophysics , quantum mechanics , magnetic field , tec
The highly nonlinear variation of the ionospheric F 2 layer critical frequency ( f o F 2 ) greatly limits the efficiency of communications, radar, and navigation systems that employ high‐frequency radio waves. This paper proposes an effective method to predict the f o F 2 using the extreme learning machine (ELM). Compared with the previous neural network model based on feedforward algorithm, the ELM model offers the advantages of faster training speed and less manual intervention. The ELM model is trained with the daily hourly values of f o F 2 at Darwin (12.4°S, 131.5°E) in Australia. The training data are selected from 1995 to 2012, except 1997 and 2000, which includes all periods of quiet and disturbed geomagnetic conditions. The f o F 2 data to verify model performance are selected in 1997, 2000, and 2013, which are low, high, and moderate solar activity years, respectively. The prediction results have shown that the proposed ELM model can achieve faster training process while maintaining the similar accuracy compared with BPNN. In addition, the proposed ELM model is compared with the International Reference Ionosphere model prediction. The ELM model predicts the f o F 2 values more accurately than the International Reference Ionosphere model in low (1997), moderate (2013), and high (2000) solar activity years, as clearly seen on the yearly root‐mean‐square error. As far as the author's knowledge, this is the first time that the ELM model is applied to predict f o F 2 .