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Reduction to Least-Squares Estimates in Multiple Fuzzy Regression Analysis

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1 Author(s)
Chi-Tsuen Yeh ; Dept. of Math. Educ., Nat. Univ. of Tainan, Tainan, Taiwan

In this paper, we deal with the problem of least-squares multiple regression with fuzzy data. The regression coefficients are assumed to be real (crisp). A formula for solving the regression coefficients in one-variable models is derived. If each independent variable is effective (i.e., its corresponding regression coefficient is nonzero), the multiple regression problem can be replaced with a 0-1 programming problem. Its optimal solution is easily computed. Finally, we also propose effective algorithms to compute the regression coefficients in a general case.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:17 ,  Issue: 4 )

Date of Publication:

Aug. 2009

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