Soft computing prediction of economic growth based in science and technology factors

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  author =       "Dusan Markovic and Dalibor Petkovic and 
                 Vlastimir Nikolic and Milos Milovancevic and Biljana Petkovic",
  title =        "Soft computing prediction of economic growth based in
                 science and technology factors",
  journal =      "Physica A: Statistical Mechanics and its
  volume =       "465",
  pages =        "217--220",
  year =         "2017",
  ISSN =         "0378-4371",
  DOI =          "doi:10.1016/j.physa.2016.08.034",
  URL =          "",
  abstract =     "The purpose of this research is to develop and apply
                 the Extreme Learning Machine (ELM) to forecast the
                 gross domestic product (GDP) growth rate. In this study
                 the GDP growth was analyzed based on ten science and
                 technology factors. These factors were: research and
                 development (R&D) expenditure in GDP, scientific
                 and technical journal articles, patent applications for
                 nonresidents, patent applications for residents,
                 trademark applications for nonresidents, trademark
                 applications for residents, total trademark
                 applications, researchers in R&D, technicians in
                 R&D and high-technology exports. The ELM results
                 were compared with genetic programming (GP), artificial
                 neural network (ANN) and fuzzy logic results. Based
                 upon simulation results, it is demonstrated that ELM
                 has better forecasting capability for the GDP growth
  keywords =     "genetic algorithms, genetic programming, Soft
                 computing, GDP, Prediction, Science and technology

Genetic Programming entries for Dusan Markovic Dalibor Petkovic Vlastimir Nikolic Milos Milovancevic Biljana Petkovic