Linear regression analysis for fuzzy input and output data using the extension principle
نویسندگان
چکیده
منابع مشابه
Fuzzy linear regression analysis for fuzzy input-output data
In this paper, we have presented a new method to evaluate fuzzy linear regression models based on Tanaka’s approach, where both input data and output data are fuzzy numbers, using Tw-based fuzzy arithmetic operations. This method simpli3es the computation of fuzzy arithmetic operations. General linear program is applied to derive the solutions. We also prove scale-independent property of our mo...
متن کاملMultiple Fuzzy Regression Model for Fuzzy Input-Output Data
A novel approach to the problem of regression modeling for fuzzy input-output data is introduced.In order to estimate the parameters of the model, a distance on the space of interval-valued quantities is employed.By minimizing the sum of squared errors, a class of regression models is derived based on the interval-valued data obtained from the $alpha$-level sets of fuzzy input-output data.Then,...
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 least-squares linear regression analysis for fuzzy input-output data
A fuzzy regression model is used in evaluating the functional relationship between the dependent and independent variables in a fuzzy environment. Most fuzzy regression models are considered to be fuzzy outputs and parameters but non-fuzzy (crisp) inputs. In general, there are two approaches in the analysis of fuzzy regression models: linear-programmingbased methods and fuzzy least-squares meth...
متن کاملFuzzy estimates of regression parameters in linear regression models for imprecise input and output data
The method for obtaining the fuzzy estimates of regression parameters with the help of “Resolution Identity” in fuzzy sets theory is proposed. The -level least-squares estimates can be obtained from the usual linear regression model by using the -level real-valued data of the corresponding fuzzy input and output data. The membership functions of fuzzy estimates of regression parameters will be ...
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: Computers & Mathematics with Applications
سال: 2003
ISSN: 0898-1221
DOI: 10.1016/s0898-1221(03)90006-x