کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
397068 1438463 2013 26 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Parametric regression analysis of imprecise and uncertain data in the fuzzy belief function framework
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Parametric regression analysis of imprecise and uncertain data in the fuzzy belief function framework
چکیده انگلیسی

In this paper, parametric regression analyses including both linear and nonlinear regressions are investigated in the case of imprecise and uncertain data, represented by a fuzzy belief function. The parameters in both the linear and nonlinear regression models are estimated using the fuzzy evidential EM algorithm, a straightforward fuzzy version of the evidential EM algorithm. The nonlinear regression model is derived by introducing a kernel function into the proposed linear regression model. An unreliable sensor experiment is designed to evaluate the performance of the proposed linear and nonlinear parametric regression methods, called parametric evidential regression (PEVREG) models. The experimental results demonstrate the high prediction accuracy of the PEVREG models in regressions with crisp inputs and a fuzzy belief function as output.


► The likelihood function is generalized in the framework of fuzzy belief function.
► A fuzzy version of evidential EM (FE2M) algorithm is proposed.
► With FE2M, a parametric evidential regression model is proposed to deal with uncertain data.
► The parametric evidential regression method has high prediction accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 54, Issue 8, October 2013, Pages 1217–1242
نویسندگان
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