fuzzy parameter estimation via fuzzy weights and linear programming

Document Type : Original Paper

Authors

1 Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Mathematics, Iran University of Science and Technology, Tehran, Iran

3 Department of Statistics, Roudehen Branch, Islamic Azad University, Roudehen, Iran

Abstract

Fuzzy regression represents the relation between variables. Fuzzy regression analysis is one of the most widely used statistical techniques. In this study, fuzzy regression is considered with the crisp input and the fuzzy output. A hybrid algorithm based on fuzzy weights and linear programming is designed for the fuzzy nonparametric regression model prediction that function form is assumed unknown and in cases low data. In proposed method, the objective function minimizes the spread of outputs. Finally, the performance of the suggested method is compared with linear programming (LP) and quadratic programming (QP) methods using the numerical examples. The results demonstrate that the proposed method has more accurate than the LP and QP methods. Also, it is verified in cases of low data.

Keywords

Main Subjects

References

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Journal of Advanced Mathematical Modeling
Volume 6, Issue 1
April 2016
Pages 61-80

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History

  • Receive Date: 08 November 2015
  • Revise Date: 18 August 2016
  • Accept Date: 08 September 2016

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