LS-SVM method for fuzzy nonlinear regression
| dc.contributor.author | Tekşen, Ümran M. | |
| dc.contributor.author | Genç, Aşır | |
| dc.date.accessioned | 2018-05-09T07:25:52Z | |
| dc.date.available | 2018-05-09T07:25:52Z | |
| dc.date.issued | 2011 | |
| dc.description | URL: http://sjam.selcuk.edu.tr/sjam/article/view/290 | en_US |
| dc.description.abstract | In this study LS-SVM method is applied for fuzzy nonlinear regression whose input and output are fuzzy numbers. The method solves any problem of classification or regression via transforming to a quadratic problem without running into local solutions. This method is favourable owing to independent from a model. In this study, two practises are applied to linear and nonlinear data. | en_US |
| dc.identifier.citation | Tekşen, Ü. M., Genç, A. (2011). LS-SVM method for fuzzy nonlinear regression. Selcuk Journal of Applied Mathematics, 53-60. | en_US |
| dc.identifier.endpage | 60 | |
| dc.identifier.issn | 1302-7980 | en_US |
| dc.identifier.startpage | 53 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/10593 | |
| dc.language.iso | en | en_US |
| dc.publisher | Selcuk University Research Center of Applied Mathematics | en_US |
| dc.relation.ispartof | Selcuk Journal of Applied Mathematics | en_US |
| dc.relation.publicationcategory | Makale - Kategori Belirlenecek | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | Fuzzy nonlineer regression | en_US |
| dc.subject | Least squares support vector machine | en_US |
| dc.subject | Bulanık doğrusal olmayan regresyon | en_US |
| dc.subject | En küçük kareler destek vektör makinesi | en_US |
| dc.title | LS-SVM method for fuzzy nonlinear regression | en_US |
| dc.type | Article | en_US |
