The ionospheric foF2 prediction based on neural network optimization algorithm

HU Xiaoxi; ZHOU Chen; ZHAO Jun; LIU Yi; LIU Moran; ZHAO Zhengyu

doi:10.13443/j.cjors.2017111301

Volume 33 Issue 6

Dec. 2018

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CHINESE JOURNAL OF RADIO SCIENCE > 2018 > 33(6): 708-716. > DOI: 10.13443/j.cjors.2017111301

HU Xiaoxi, ZHOU Chen, ZHAO Jun, LIU Yi, LIU Moran, ZHAO Zhengyu. The ionospheric f_oF₂ prediction based on neural network optimization algorithm[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2018, 33(6): 708-716. doi: 10.13443/j.cjors.2017111301

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The ionospheric f_oF₂ prediction based on neural network optimization algorithm

1.
WuHan University, Ionospheric Laboratory, Wuhan 430072, China
2.
China Research Institute of Radiowave Propagation, Qingdao 266107, China

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Received Date: November 12, 2017
Available Online: December 30, 2020
Published Date: December 29, 2018

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Abstract

Abstract

In order to improve the ionospheric critical frequency based on BP neural network (f_oF₂) prediction accuracy, we adopt a method of improved particle swarm optimization neural network method to optimize the initial weights of BP network to prevent the emergence of local optimal neural network in training. By comparing the results of neural network prediction based on particle swarm optimization and the optimization of genetic algorithm, we find that the two methods have good performance for BP neural network. In addition, compared with the international reference ionosphere model (IRI2016), we find that the adaptive mutation particle swarm optimization neural network can effectively improve the prediction accuracy of f_oF₂, and has better prediction effect in low latitude area.
- particle swarm optimization algorithm,
- genetic algorithm,
- BP neural networks,
- IRI2016,
- ionosphere f_oF₂ prediction

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The ionospheric f_oF₂ prediction based on neural network optimization algorithm