Time series modeling of ionosphere total electron content using wavelet neural network and hybrid PSO training algorithm
| Autor: | Mir Reza Ghaffari Razin, Behzad Voosoghi |
|---|---|
| Jazyk: | perština |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | فصلنامه علوم و فناوری فضایی, Vol 13, Iss 3, Pp 39-50 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2008-4560 2423-4516 |
| DOI: | 10.30699/jsst.2021.1204 |
| Popis: | In this paper, WNN with PSO training algorithm is used to modeling and prediction of time-dependent ionosphere total electron content (TEC) variations. 2 different combinations of input observations are evaluated. The number of stations used to train of WNN with PSO algorithm selected 20 and 10. In all testing mode, 3 GPS stations with proper distribution are considered as a testing stations. Statistical indicators relative error, dVTEC and correlation coefficient were used to assess the wavelet neural network model. The results of proposed model compared with GPS-TEC and international reference ionosphere 2012 (IRI-2012) TEC. Average relative error computed in 3 test stations are 5.43% with 20 training station and 9.05% with 10 training station. Also the correlation coefficient calculated in 3 test stations are 0.954 with 20 training station and 0.907 with 10 training station. The results of this study show that the WNN with PSO algorithm is a reliable model to predict the temporal variations in the ionosphere. |
| Databáze: | Directory of Open Access Journals |
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