A Back-Propagation Artificial Neural Network Approach for Three-Dimensional Coordinate Transformation
Yükleniyor...
Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Academic Journals
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The European Datum 1950 (ED50) of the Turkish national geodetic network (TNGN) and the World Geodetic System 1984 (WGS84) of the Turkish national fundamental GPS network (TNFGN) are in use as geodetic reference frames in Turkey. According to the use of two reference systems, it is necessary to transform the three-dimensional (3D) coordinate data from ED50 to WGS84 or vice versa. The seven-parameter similarity transformation method is frequently used for 3D coordinate transformation in geodesy. In this study, a back propagation artificial neural network (BPANN) that has been more widely applied in engineering among all other neural network models is evaluated as an alternative 3D coordinate transformation method. BPANN is compared with a popular seven-parameter similarity transformation (Molodensky-Badekas) method over a test area, in terms of root mean square error (RMSE). The results indicated that the employment of BPANN transformed 3D coordinates (X, Y, Z) more accurate than Molodensky-Badekas method and can be useful for 3D coordinate transformation between ED50 and WGS84.
Açıklama
Anahtar Kelimeler
3d coordinate transformation, Back propagation artificial neural network, Seven-parameter similarity transformation, Bpann, Molodensky-badekas
Kaynak
Scientific Research and Essays
WoS Q Değeri
Q3
Scopus Q Değeri
N/A
Cilt
5
Sayı
21
Künye
Turgut, B., (2010). A Back-Propagation Artificial Neural Network Approach for Three-Dimensional Coordinate Transformation. Scientific Research and Essays, 5(21), 3330-3335.
