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Article detail · 2010

Coordinate transformation by radial basis function neural network

Journal

Scientific Research and Essays
OpenAlex Open access · green SJR Q3 JCR Q3 Citations 25 Percentile 85.0% FWCI 1.78
Year
2010
Type
article

Data source split

  • YÖKSİS venue Scientific Research and Essays
  • OpenAlex OpenAlex enrichment (abstract, citations, topics)

Abstract

OpenAlex · English

The Turkish National Geodetic Network (TNGN) datum (ED50) was changed to the Turkish National Fundamental GPS Network (TNFGN) datum (WGS84) in 2001 in parallel with the increasing use of GPS technology. Due to this reference frame change it became necessary to transform the existing coordinate information between ED50 and WGS84. The two-dimensional (2D) affine transformation is widely used for coordinate transformation. The objective of this study is proposing a radial basis function neural network (RBFNN) that has been more widely applied in function approximation as an alternative coordinate transformation method. 2D affine transformation (Affine) method and RBFNN are evaluated over a study area, in terms of the root mean square error (RMSE). The results showed that RBFNN transformed the plane coordinates (Y, X) of the check points with a better accuracy (± 0.011 m, ± 0.013 m, respectively) than Affine method and pointed out that RBFNN can be used for coordinate transformation.   Key words: Coordinate transformation, artificial neural network, radial basis function, affine transformation.

Topics

Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

25 citations

OpenAlex cited_by_count (cache / database)

Authors

  1. MEVLÜT GÜLLÜ AFYON KOCATEPE ÜNİVERSİTESİ