کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10694097 | 1019996 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography
ترجمه فارسی عنوان
کاربرد منطقه ای چند لایه شبکه های عصبی مصنوعی در توموگرافی سه بعدی ایونوسفر
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موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علوم فضا و نجوم
چکیده انگلیسی
Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17Â ÃÂ 1011 (electrons/m3) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advances in Space Research - Volume 58, Issue 3, 1 August 2016, Pages 339-348
Journal: Advances in Space Research - Volume 58, Issue 3, 1 August 2016, Pages 339-348
نویسندگان
Mir Reza Ghaffari Razin, Behzad Voosoghi,