Determining the most relevant input parameter set by using extreme learning machine

Semra Gündüç; Recep Eryigit

doi:10.33769/aupse.525325

Research Article

Year 2021, Volume: 63 Issue: 1, 25 - 31, 30.06.2021

Semra Gündüç Recep Eryigit

https://doi.org/10.33769/aupse.525325

Abstract

References

Rakic, G., Milenkovic, D., Vujovic, S., Vujovic, T., Jovic, S., Information system for e-GDP based on computational intelligence approach, Physica A: Statistical Mechanics and its Applications, 513 (2019), 418–423. https://doi.org/ 10.1016/j.physa.2018.09.010
Markovic, D., Petkovic, D., Nikolic, V., Milovancevc, M., Petkovic, B., Soft computing prediction of economic growth based in science and technology factors, Physica A: Statistical Mechanics and its Applications, 465 (2017), 217-220. https://doi.org/10.1016/j.physa.2016.08.034
Alshehry, A.S., Belloumi, M., Energy consumption, carbon dioxide emis- sions and economic growth: The case of Saudi Arabia, Renewable and Sustainable Energy Reviews, 41 (2015), 237-247. https://doi.org/10.1016/j.rser.2014.08.004
Mbarek, M.B., Abdelkafi, I., Feki, R., Nonlinear causality between renewable energy, economic growth, and unemployment: Evidence from Tunisia, Journal of Knowledge Economy, 19 (2018), 694-702. https://doi.org/10.1007/s13132-016-0357-9
Huang, G.B., Zhu, Q.Y., Siew, C.K., Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126
World Bank Open Data, https://data.worldbank.org
Kankal, M., Uzlu, E., Neural network approach with teachinglearning- based optimization for modeling and forecasting long-term electric energy demand in Turkey, Neural Computing Application, 28 (2017), 737-747. https://doi.org/10.1007/s00521-016-2409-2
Cogoljevic, D., Alizamir, M., Piljan, I., Piljan, T., Prljicc, K. , Zimonjic, S., A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, 495 (2018), 211-214. https://doi.org/10.1016/j.physa.2017.12.082
Milacic, L., Jovic, S., Vuovcc, T., Miljkov, J., Application of artificial neu- ral network with extreme learning machine for economic growth estimation, Physica A: Statistical Mechanics and its Applications, 465 (2017), 285-288. https://doi.org/10.1016/j.physa.2016.08.040

Determining the most relevant input parameter set by using extreme learning machine

Year 2021, Volume: 63 Issue: 1, 25 - 31, 30.06.2021

Semra Gündüç Recep Eryigit

https://doi.org/10.33769/aupse.525325

Abstract

In this work, Extreme Learning Machine (ELM) algorithm is used to estimate the GDP per capita. The amount of electricity production, from four different sources, is chosen as input parameters. To find out the most relevant input data for a reasonable estimation of GDP, different sources introduced separately to ELM. By following the coefficient of determination of estimation, by trial and error, results are obtained. The residuals are also given to show that model perform well. Renewable energy sources produce the best results in the estimation of GDP.

Keywords

Gross domestic product estimation, extreme learning machine, electricity production sources

References

Rakic, G., Milenkovic, D., Vujovic, S., Vujovic, T., Jovic, S., Information system for e-GDP based on computational intelligence approach, Physica A: Statistical Mechanics and its Applications, 513 (2019), 418–423. https://doi.org/ 10.1016/j.physa.2018.09.010
Markovic, D., Petkovic, D., Nikolic, V., Milovancevc, M., Petkovic, B., Soft computing prediction of economic growth based in science and technology factors, Physica A: Statistical Mechanics and its Applications, 465 (2017), 217-220. https://doi.org/10.1016/j.physa.2016.08.034
Alshehry, A.S., Belloumi, M., Energy consumption, carbon dioxide emis- sions and economic growth: The case of Saudi Arabia, Renewable and Sustainable Energy Reviews, 41 (2015), 237-247. https://doi.org/10.1016/j.rser.2014.08.004
Mbarek, M.B., Abdelkafi, I., Feki, R., Nonlinear causality between renewable energy, economic growth, and unemployment: Evidence from Tunisia, Journal of Knowledge Economy, 19 (2018), 694-702. https://doi.org/10.1007/s13132-016-0357-9
Huang, G.B., Zhu, Q.Y., Siew, C.K., Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126
World Bank Open Data, https://data.worldbank.org
Kankal, M., Uzlu, E., Neural network approach with teachinglearning- based optimization for modeling and forecasting long-term electric energy demand in Turkey, Neural Computing Application, 28 (2017), 737-747. https://doi.org/10.1007/s00521-016-2409-2
Cogoljevic, D., Alizamir, M., Piljan, I., Piljan, T., Prljicc, K. , Zimonjic, S., A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, 495 (2018), 211-214. https://doi.org/10.1016/j.physa.2017.12.082
Milacic, L., Jovic, S., Vuovcc, T., Miljkov, J., Application of artificial neu- ral network with extreme learning machine for economic growth estimation, Physica A: Statistical Mechanics and its Applications, 465 (2017), 285-288. https://doi.org/10.1016/j.physa.2016.08.040

There are 9 citations in total.

Details

Primary Language	English
Subjects	Engineering
Journal Section	Research Articles
Authors	Semra Gündüç 0000-0002-3811-9547 Recep Eryigit This is me 0000-0002-4282-6340
Publication Date	June 30, 2021
Submission Date	February 11, 2019
Acceptance Date	February 3, 2021
Published in Issue	Year 2021 Volume: 63 Issue: 1

Cite

APA	Gündüç, S., & Eryigit, R. (2021). Determining the most relevant input parameter set by using extreme learning machine. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 63(1), 25-31. https://doi.org/10.33769/aupse.525325
AMA	Gündüç S, Eryigit R. Determining the most relevant input parameter set by using extreme learning machine. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. June 2021;63(1):25-31. doi:10.33769/aupse.525325
Chicago	Gündüç, Semra, and Recep Eryigit. “Determining the Most Relevant Input Parameter Set by Using Extreme Learning Machine”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63, no. 1 (June 2021): 25-31. https://doi.org/10.33769/aupse.525325.
EndNote	Gündüç S, Eryigit R (June 1, 2021) Determining the most relevant input parameter set by using extreme learning machine. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63 1 25–31.
IEEE	S. Gündüç and R. Eryigit, “Determining the most relevant input parameter set by using extreme learning machine”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 63, no. 1, pp. 25–31, 2021, doi: 10.33769/aupse.525325.
ISNAD	Gündüç, Semra - Eryigit, Recep. “Determining the Most Relevant Input Parameter Set by Using Extreme Learning Machine”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63/1 (June 2021), 25-31. https://doi.org/10.33769/aupse.525325.
JAMA	Gündüç S, Eryigit R. Determining the most relevant input parameter set by using extreme learning machine. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2021;63:25–31.
MLA	Gündüç, Semra and Recep Eryigit. “Determining the Most Relevant Input Parameter Set by Using Extreme Learning Machine”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 63, no. 1, 2021, pp. 25-31, doi:10.33769/aupse.525325.
Vancouver	Gündüç S, Eryigit R. Determining the most relevant input parameter set by using extreme learning machine. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2021;63(1):25-31.