Bicriteria variable selection in a fuzzy regression equation
HF Wang, RC Tsaur - Computers & mathematics with Applications, 2000 - Elsevier
HF Wang, RC Tsaur
Computers & mathematics with Applications, 2000•ElsevierBy considering two criteria of minimum total sum of vagueness and minimum total sum of
squares in estimation, this article proposes a variable selection method for a fuzzy
regression equation with crisp-input and fuzzy-output. A branch-and-bound algorithm is
designed and “the least resistance principle” is adopted to determine the set of
compromised solutions. Numerical examples are provided for illustration.
squares in estimation, this article proposes a variable selection method for a fuzzy
regression equation with crisp-input and fuzzy-output. A branch-and-bound algorithm is
designed and “the least resistance principle” is adopted to determine the set of
compromised solutions. Numerical examples are provided for illustration.
By considering two criteria of minimum total sum of vagueness and minimum total sum of squares in estimation, this article proposes a variable selection method for a fuzzy regression equation with crisp-input and fuzzy-output. A branch-and-bound algorithm is designed and “the least resistance principle” is adopted to determine the set of compromised solutions. Numerical examples are provided for illustration.
Elsevier
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