Parameter Estimation by Fuzzy Adaptive Networks and Comparison with Robust Regression Methods
Yazarlar (2)
Türkan Erbay Dalkılıç
Karadeniz Technical University, Türkiye
Prof. Dr. Kamile ŞANLI KULA
Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (ESCI dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Gazi University Journal of Science | ||
| Dergi ISSN | 2147-1762 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | ESCI: Emerging Sources Citation Index | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2015 |
| Cilt / Sayı / Sayfa | 28 / 1 / 103–113 | DOI | – |
| Özet |
| Fuzzy adaptive networks used for estimating the unknown parameters of a regression model are based on fuzzy ifthen rules and a fuzzy inference system. In regression analysis, data analysis is very important, because, every observation may have a large influence on the parameters estimates in the regression model. When a data set has outliers, robust methods such as the M method (Huber, Hampel, Andrews and Tukey), Least Median of Squares (LMS) and Reweighed Least Squares Based on the LMS (RLS) are used for estimating parameters. In this study, a method and an algorithm have been suggested to define the parameters of a switching regression model. Adaptive networks have been used in constructing one model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, to define the optimal class number of independent variables, we aimed to use the suggested validity criterion. The proposed method has the properties of a robust method, because the process does not give permission to the intuitional and is not affected by the outliers, which exist in the independent variable. Consequently, another aim of this study is, to compare the proposed method with the robust methods that are mentioned above. For the comparison the cross-validation method is used. |
| Anahtar Kelimeler |
| Fuzzy adaptive network | Robust regression | Switching regression |
