Abstract
Fuzzy regression analysis plays an important role in analyzing the correlation between the dependent and explanatory variables in the fuzzy system. This paper put forward the FLS (Fuzzy Least Squares) method for parameter estimating of the fuzzy linear regression model with input, output variables and regression coefficients that are normal fuzzy numbers. Our improved method proves the statistical properties, i.e., linearity and unbiasedness of the fuzzy least square estimators. Residuals, residual sum of squares and coefficient of determination are given to illustrate the fitting degree of the regression model. Finally, the method is validated in both rationality and validity by solving a practical parameter estimation problem.
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Acknowledgments
This work is supported in part by the Science and Technology Department of Henan Province (Grant No. 152300410230) and the Key Scientific Research Projects of Henan Province (Grant No. 17A110040).
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Gu, CL., Wang, W., Wei, HY. (2017). Regression Analysis Model Based on Normal Fuzzy Numbers. In: Fan, TH., Chen, SL., Wang, SM., Li, YM. (eds) Quantitative Logic and Soft Computing 2016. Advances in Intelligent Systems and Computing, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-319-46206-6_46
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DOI: https://doi.org/10.1007/978-3-319-46206-6_46
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