نوع مقاله : مقالة تحقیقی (پژوهشی)
نویسندگان
1 گروه مهندسی نقشهبرداری، دانشکده علوم زمین، دانشگاه صنعتی اراک، اراک، ایران
2 گروه ژئودزی، دانشکده مهندسی نقشه برداری، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران ایران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
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.
کلیدواژهها [English]