Fuzzy linear least squares for the identification of possibilistic regression models

DOI10.1016/j.fss.2018.10.003zbMath1426.62218OpenAlexW2896492993WikidataQ129104619 ScholiaQ129104619MaRDI QIDQ2328946

Dominik Hose, Michael Hanss

Publication date: 16 October 2019

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2018.10.003

zbMATH Keywords

fuzzy least squaresfuzzy linear regressionpossibilistic statisticsfuzzy quasi inverseinverse fuzzy arithmeticpossibilistic regression model

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Fuzziness, and linear inference and regression (62J86)

Related Items (3)

A universal approach to imprecise probabilities in possibility theory ⋮ Fully decoupled reliability-based design optimization of structural systems subject to uncertain loads ⋮ On data-based estimation of possibility distributions

Uses Software

• CVX
• qpOASES

Cites Work

• Fuzzy least squares
• Possibilistic linear regression analysis for fuzzy data
• qpOASES: a parametric active-set algorithm for~quadratic programming
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• Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
• Possibility theory and statistical reasoning
• Fuzzy data analysis by possibilistic linear models
• When upper probabilities are possibility measures
• The concept of a linguistic variable and its application to approximate reasoning. I
• A least-squares approach to fuzzy linear regression analysis.
• A fuzzy linear regression model with better explanatory power
• Theory and practice of uncertain programming
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• Uncertainty-based information. Elements of generalized information theory.
• From fuzzy regression to gradual regression: interval-based analysis and extensions
• A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory
• Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
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