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

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@Article{Markovic:2017:PASMA,

• 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 Applications",
• volume = "465",
• pages = "217--220",
• year = "2017",
• ISSN = "0378-4371",
• DOI = "doi:10.1016/j.physa.2016.08.034",
• URL = "http://www.sciencedirect.com/science/article/pii/S0378437116305519",
• 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 rate.",
• keywords = "genetic algorithms, genetic programming, Soft computing, GDP, Prediction, Science and technology factor",

}

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

Citations