Soft computing prediction of economic growth based in science and technology factors
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- @Article{Markovic:2017:PASMA,
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author = "Dusan Markovic and Dalibor Petkovic and
Vlastimir Nikolic and Milos Milovancevic and Biljana Petkovic",
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title = "Soft computing prediction of economic growth based in
science and technology factors",
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journal = "Physica A: Statistical Mechanics and its
Applications",
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volume = "465",
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pages = "217--220",
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year = "2017",
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ISSN = "0378-4371",
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DOI = "doi:10.1016/j.physa.2016.08.034",
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URL = "http://www.sciencedirect.com/science/article/pii/S0378437116305519",
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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.",
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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