Open AccessPrediction high frequency parameters based on neural network
Author(s) -
Wenhe Zhang,
Gongsheng Huang,
Guisheng Wang,
Ye Wang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/631/5/052035
Subject(s) - computer science , artificial neural network , value (mathematics) , recurrent neural network , mean squared prediction error , series (stratigraphy) , time series , data mining , artificial intelligence , algorithm , machine learning , paleontology , biology
Aiming at the shortcomings of the current high frequency communication frequency parameter prediction method, the frequency parameter prediction method based on Gated Recurrent Unit Recurrent Neural Networks (GRU RNN) is proposed. Through the analysis of the ionospheric parameter f0F2 data, the GRU can predict the f0F2 value by long-term memory of the historical data when processing the time series related data. Compared with other prediction methods, the error between the predicted value and the true value is only 2%. The research results show that the model to predict the f0F2 value in advance is feasible.