Robust Fuzzy Varying Coefficient Regression Analysis with Crisp Inputs and Gaussian Fuzzy Output
نویسندگان
چکیده
منابع مشابه
Robust Fuzzy Varying Coefficient Regression Analysis with Crisp Inputs and Gaussian Fuzzy Output
This study presents a fuzzy varying coefficient regression model after deleting the outliers to improve the feasibility and effectiveness of the fuzzy regression model. The objective of our methodology is to allow the fuzzy regression coefficients to vary with a covariate, and simultaneously avoid the impact of data contaminated by outliers. In this paper, fuzzy regression coefficients are repr...
متن کاملA New Algorithm for Fuzzy Linear Regression with Crisp Inputs and Fuzzy Output
In this work, the parameters of fuzzy linear regression based on the least squares approach is computed by ST-decomposition method. This method is not an iterative technique, however, it is a powerful method for nonsingular coefficient matrices. Numerical examples are at the end of this paper to illustrate the performance of the new method.
متن کاملTwo-Parameters Fuzzy Ridge Regression with Crisp Input and Fuzzy Output
In this paper a new weighted fuzzy ridge regression method for a given set of crisp input and triangular fuzzy output values is proposed. In this regard, ridge estimator of fuzzy parameters is obtained for regression model and its prediction error is calculated by using the weighted fuzzy norm of crisp ridge coefficients. . To evaluate the proposed regression model, we introduce the fu...
متن کاملLinear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
In order to estimate fuzzy regression models, possibilistic and least-squares procedures can be considered. By taking into account a least-squares approach, regression models with crisp or fuzzy inputs and crisp or fuzzy output are suggested. In particular, for these fuzzy regression models, unconstrained and constrained (with inequality restrictions) least-squares estimation procedures are dev...
متن کاملFuzzy Robust Regression Analysis with Fuzzy Response Variable and Fuzzy Parameters Based on the Ranking of Fuzzy Sets
Robust regression is an appropriate alternative for ordinal regression when outliers exist in a given data set. If we have fuzzy observations, using ordinal regression methods can't model them; In this case, using fuzzy regression is a good method. When observations are fuzzy and there are outliers in the data sets, using robust fuzzy regression methods are appropriate alternatives....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computing Science and Engineering
سال: 2013
ISSN: 1976-4677
DOI: 10.5626/jcse.2013.7.4.263