Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan

Details Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan

Jun 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009adspr..43.1780l&link_type=abstract

Advances in Space Research, Volume 43, Issue 11, p. 1780-1785.

Computer Science

Performance

Scientific paper

Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F2 layer critical frequency(foF2), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of foF2 with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of foF2 are improved.

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