Information system for e-GDP based on computational intelligence approach
Tanja Vujovic and
Physica A: Statistical Mechanics and its Applications, 2019, vol. 513, issue C, 418-423
Information system for electronic commerce could be used for selling of goods and services online. However information system could be used for gross domestic product (GDP) estimation and analyzing based on different input parameters. GDP is considered as the main indicator for economic growth and there is need for more advanced way of GDP estimation. Therefore in this study was made an attempt to design a system for GDP estimation based on several economic parameters. The system is called e-GDP (electronic GDP) system and the main part of the system is e-GDP module. The e-GDP module performs estimation of GDP based in the given inputs. The estimation and calculation of GDP is based on computational intelligence approach namely extreme learning machine — ELM. ELM present a training algorithm for neural networks which could be more precise than traditional training algorithms and training time is reduced. As the input parameters there are trade, trade in services, merchandise trade, exports and imports. The system is designed based on object-oriented methodology. Therefore the object-oriented paradigm was established to perform e-GDP designing and analyzing. Obtained results shown reliability of the used approach for the GDP estimation.
Keywords: Gross domestic product (GDP); Economic growth; e-GDP; Information system; Extreme learning machine (ELM) (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:513:y:2019:i:c:p:418-423
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().