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A robust least squares fuzzy regression model based on kernel function | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 17، شماره 4، مهر و آبان 2020، صفحه 105-119 اصل مقاله (737.52 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2020.5409 | ||
| نویسندگان | ||
| A. H. Khammar* ؛ M. Arefi؛ M. G. Akbari | ||
| Department of Statistics, Faculty of Mathematical Sciences and Statistics, University of Birjand, Birjand, Iran | ||
| چکیده | ||
| In this paper, a new approach is presented to fit a robust fuzzy regression model based on some fuzzy quantities. In this approach, we first introduce a new distance between two fuzzy numbers using the kernel function, and then, based on the least squares method, the parameters of fuzzy regression model is estimated. The proposed approach has a suitable performance to present the robust fuzzy model in the presence of different types of outliers. Using some simulated data sets and some real data sets, the application of the proposed approach in modeling some characteristics with outliers, is studied. | ||
| کلیدواژهها | ||
| Distance؛ kernel function؛ least squares method؛ outliers؛ robust fuzzy regression | ||
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آمار تعداد مشاهده مقاله: 775 تعداد دریافت فایل اصل مقاله: 727 |
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