A weighted goal programming approach to estimate the linear regression model in full quasi type-2 fuzzy environment
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Publication:2988535
DOI10.3233/IFS-152046zbMath1361.62043OpenAlexW2324733101MaRDI QIDQ2988535
Mohsen Arefi, E. Hosseinzadeh, Hassan Hassanpour
Publication date: 19 May 2017
Published in: Journal of Intelligent & Fuzzy Systems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3233/ifs-152046
Linear regression; mixed models (62J05) Multi-objective and goal programming (90C29) Fuzziness, and linear inference and regression (62J86)
Related Items (2)
Estimating the parameters of fuzzy linear regression model with crisp inputs and Gaussian fuzzy outputs: a goal programming approach ⋮ Quantile fuzzy regression based on fuzzy outputs and fuzzy parameters
Cites Work
- Unnamed Item
- Unnamed Item
- Fuzzy least squares
- A goal programming approach to fuzzy linear regression with fuzzy input-output data
- Fuzzy least-absolutes regression using shape preserving operations
- Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric
- Fuzzy regression based on asymmetric support vector machines
- Least-squares approach to regression modeling in full interval-valued fuzzy environment
- Fuzzy data analysis by possibilistic linear models
- The concept of a linguistic variable and its application to approximate reasoning. I
- A weighted goal programming approach to fuzzy linear regression with crisp inputs and type-2 fuzzy outputs
- Some algebraic properties and a distance measure for interval-valued fuzzy numbers
- Least-squares estimates in fuzzy regression analysis.
- A mathematical-programming approach to fuzzy linear regression analysis
- Some properties of fuzzy sets of type 2
- A GOAL PROGRAMMING APPROACH TO FUZZY LINEAR REGRESSION WITH NON-FUZZY INPUT AND FUZZY OUTPUT DATA
- Resolution of fuzzy regression model
- Operations on type-2 fuzzy sets
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