Application of modified genetic programming algorithm for identification of mathematical models through the expansion of the training set by neural network

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Zvezintsev:2013:VASTU,
  author =       "Andrey Igorevich Zvezintsev and 
                 Irina Yurievna Kvyatkovskaya",
  title =        "Application of modified genetic programming algorithm
                 for identification of mathematical models through the
                 expansion of the training set by neural network",
  journal =      "Vestnik of Astrakhan State Technical University.
                 Series: Management, Computer science and Informatics",
  year =         "2013",
  volume =       "2013",
  number =       "2",
  pages =        "58--65",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, mathematical
                 identification, artificial neural network,
                 approximation, knowledge extraction, mathematical
                 model.",
  publisher =    "Astrakhan State Technical University",
  ISSN =         "2072-9502",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:ce1ef0f63b4866b39a7f5dcf671485b9",
  source =       "Vestnik Astrahanskogo Gosudarstvennogo Tehni{\v
                 c}eskogo Universiteta. Seri{\^a}: Upravlenie, Vy{\v
                 c}islitel{'}na{\^a} Tehnika i Informatika",
  URL =          "http://vestnik.astu.org/Pages/Show/85",
  URL =          "http://vestnik.astu.org/Content/UserImages/file/inform_2013_2/07.pdf",
  size =         "8 pages",
  abstract =     "The concept of mathematical identification, its scope
                 and stages of implementation are considered. The
                 methods of identification of mathematical models:
                 regression analysis, harmonic analysis, group method of
                 data handling, genetic programming are analysed. The
                 restriction of the use of genetic programming method
                 for the identification of the mathematical model of
                 unexplored process in the presence of the noise
                 component in the experimental data is studied. Proposes
                 a modification of the method of genetic programming
                 using the method of pre-approximation and expanding the
                 training set by artificial neural network. The
                 interfaces of the developed soft-ware product and the
                 test results of the proposed method are presented.",
  notes =        "In Russian",
}

Genetic Programming entries for Andrey Igorevich Zvezintsev Irina Yurievna Kvyatkovskaya

Citations