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A Short-Term Forecast Model of foF2 Based on Elman Neural Network

1
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
2
China Research Institute of Radiowave Propagation, Qingdao 266107, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2782; https://doi.org/10.3390/app9142782
Received: 8 June 2019 / Revised: 2 July 2019 / Accepted: 9 July 2019 / Published: 10 July 2019
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Abstract

The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station. View Full-Text
Keywords: foF2; Elman neural network; improved particle swarm optimization; forecast foF2; Elman neural network; improved particle swarm optimization; forecast
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Fan, J.; Liu, C.; Lv, Y.; Han, J.; Wang, J. A Short-Term Forecast Model of foF2 Based on Elman Neural Network. Appl. Sci. 2019, 9, 2782.

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