Determining the most relevant input parameter set by using extreme learning machine
| dc.contributor.author | Gündüç, Semra | |
| dc.contributor.author | Eryiğit, Recep | |
| dc.contributor.department | Bilgisayar Mühendisliği | tr_TR |
| dc.contributor.faculty | Mühendislik Fakültesi | tr_TR |
| dc.date.accessioned | 2021-12-01T12:22:40Z | |
| dc.date.available | 2021-12-01T12:22:40Z | |
| dc.date.issued | 2021-06-30 | |
| dc.description.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. | tr_TR |
| dc.description.index | Trdizin | tr_TR |
| dc.identifier.endpage | 31 | tr_TR |
| dc.identifier.issn/e-issn | 2618-6462 | |
| dc.identifier.issue | 1 | tr_TR |
| dc.identifier.startpage | 25 | tr_TR |
| dc.identifier.uri | http://hdl.handle.net/20.500.12575/76571 | |
| dc.identifier.volume | 63 | tr_TR |
| dc.language.iso | en | tr_TR |
| dc.publisher | Ankara Üniversitesi Fen Fakültesi | tr_TR |
| dc.relation.journal | Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering | tr_TR |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | tr_TR |
| dc.subject | Gross domestic product estimation | tr_TR |
| dc.subject | Extreme learning machine | tr_TR |
| dc.subject | Electricity production sources | tr_TR |
| dc.title | Determining the most relevant input parameter set by using extreme learning machine | tr_TR |
| dc.type | Article | tr_TR |