Information Sciences: an International Journal
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Multiobjective fuzzy linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems
Fuzzy linear regression analysis for fuzzy input-output data
Information Sciences: an International Journal
On a class of fuzzy c-numbers clustering procedures for fuzzy data
Fuzzy Sets and Systems
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
A least-squares approach to fuzzy linear regression analysis
Computational Statistics & Data Analysis
General fuzzy piecewise regression analysis with automatic change-point detection
Fuzzy Sets and Systems
Fuzzy least-squares linear regression analysis using shape preserving operations
Information Sciences—Informatics and Computer Science: An International Journal
Modeling temporal functions with granular regression and fuzzy rules
Fuzzy Sets and Systems - Information processing
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Extended fuzzy regression models using regularization method
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy nonparametric regression based on local linear smoothing technique
Information Sciences: an International Journal
Using trapezoids for representing granular objects: Applications to learning and OWA aggregation
Information Sciences: an International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Information Sciences: an International Journal
Regression with fuzzy random data
Computational Statistics & Data Analysis
Dual models for possibilistic regression analysis
Computational Statistics & Data Analysis
Least squares estimation of a linear regression model with LR fuzzy response
Computational Statistics & Data Analysis
Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable
Computational Statistics & Data Analysis
Information Sciences: an International Journal
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Granular clustering: a granular signature of data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Interval regression analysis by quadratic programming approach
IEEE Transactions on Fuzzy Systems
Nonlinear internal model control: application of inverse model based fuzzy control
IEEE Transactions on Fuzzy Systems
Inverse controller design for fuzzy interval systems
IEEE Transactions on Fuzzy Systems
MIN and MAX Operators for Fuzzy Intervals and Their Potential Use in Aggregation Operators
IEEE Transactions on Fuzzy Systems
Gradual Numbers and Their Application to Fuzzy Interval Analysis
IEEE Transactions on Fuzzy Systems
Fuzzy Regression Analysis by Support Vector Learning Approach
IEEE Transactions on Fuzzy Systems
Robust fuzzy regression analysis
Information Sciences: an International Journal
A Midpoint--Radius approach to regression with interval data
International Journal of Approximate Reasoning
Fuzzy least-absolutes regression using shape preserving operations
Information Sciences: an International Journal
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Kernel based nonlinear fuzzy regression model
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
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Conventional Fuzzy regression using possibilistic concepts allows the identification of models from uncertain data sets. However, some limitations still exist. This paper deals with a revisited approach for possibilistic fuzzy regression methods. Indeed, a new modified fuzzy linear model form is introduced where the identified model output can envelop all the observed data and ensure a total inclusion property. Moreover, this model output can have any kind of spread tendency. In this framework, the identification problem is reformulated according to a new criterion that assesses the model fuzziness independently from the collected data distribution. The potential of the proposed method with regard to the conventional approach is illustrated by simulation examples.