This work introduces how possibilistic regression can be used in the case of non symmetrical tria... more This work introduces how possibilistic regression can be used in the case of non symmetrical triangular membership functions, building a system of regressions, so that suitable restrictions for each particular problem can be incorporated. We apply this ...
Fuzzy regression models has been traditionally considered as a problem of linear programming. We ... more Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great ...
Fuzzy regression models has been traditionally considered as a problem of linear programming. We ... more Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great amplitude of wide variety. We verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this problem.
This work introduces how possibilistic regression can be used in the case of non symmetrical tria... more This work introduces how possibilistic regression can be used in the case of non symmetrical triangular membership functions, building a system of regressions, so that suitable restrictions for each particular problem can be incorporated. We apply this ...
Fuzzy regression models has been traditionally considered as a problem of linear programming. We ... more Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great ...
Fuzzy regression models has been traditionally considered as a problem of linear programming. We ... more Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great amplitude of wide variety. We verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this problem.
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