Correction of Gravimetric Geoid Using Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Palancz:2015:MG,
  author =       "B. Palancz and J. L. Awange and L. Volgyesi",
  title =        "Correction of Gravimetric Geoid Using Symbolic
                 Regression",
  journal =      "Mathematical Geosciences",
  year =         "2015",
  volume =       "47",
  number =       "7",
  pages =        "867--883",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression, ANN, Artificial neural networks, Pareto
                 optimality, Geoid, GPS",
  ISSN =         "1874-8953",
  DOI =          "doi:10.1007/s11004-014-9577-3",
  size =         "17 pages",
  abstract =     "In this study, the problem of geoid correction based
                 on GPS ellipsoidal height measurements is solved via
                 symbolic regression (SR). In this case, when the
                 quality of the approximation is overriding, SR
                 employing Keijzer expansion \cite{keijzer03} to
                 generate initial trial function population can
                 supersede traditional techniques, such as parametric
                 models and soft computing models (e.g., artificial
                 neural network approach with different activation
                 functions). To demonstrate these features, numerical
                 computations for correction of the Hungarian geoid have
                 been carried out using the DataModeler package of
                 Mathematica. Although the proposed SR method could
                 reduce the average error to a level of 1--2cm, it has
                 two handicaps. The first one is the required high
                 computation power, which can be eased by the employment
                 of parallel computation via multicore processor. The
                 second one is the proper selection of the initial
                 population of the trial functions. This problem may be
                 solved via intelligent generation technique of this
                 population (e.g., Keijzer-expansion).",
  notes =        "Author Affiliations

                 Department of Photogrammetry and Geoinformatics,
                 Budapest University of Technology and Economics,
                 Budapest, 1521, Hungary

                 Western Australian Centre for Geodesy and the Institute
                 for Geoscience Research, Curtin University, Perth,
                 Australia

                 Department of Geodesy and Surveying, Budapest
                 University of Technology and Economics, Budapest, 1521,
                 Hungary",
}

Genetic Programming entries for Bela Palancz Joseph L Awange Lajos Volgyesi

Citations