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Fuzzy Information and Engineering 2023, 15(2): 87-99 https://doi.org/10.26599/FIE.2023.9270008
Article | Open Access | Issue | Published: 30 June 2023

An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data

Show Author's Information Mahdi Danesh1,  Sedigheh Danesh2,  Ali Maleki3( ),  Tahereh Razzaghnia4
Department of Industrial, Mechanics, and Aerospace Engineering, Buein Zahra Technical University, Ghazvin 3451745346, Iran
Samaneh Yaran Danesh Electronic, Tabriz 51368, Iran
Department of Statistics, West Tehran Branch, Islamic Azad University, Tehran 15614, Iran
Department of Statistics, North Tehran Branch, Islamic Azad University, Tehran 15614, Iran
Keywords:
surface roughness, linear programming, uncertainty, grinding, fuzzy regression, quadratic programming, adaptive fuzzy neural networks
Cite this article:
Danesh M, Danesh S, Maleki A, et al. An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data. Fuzzy Information and Engineering, 2023, 15(2): 87-99. https://doi.org/10.26599/FIE.2023.9270008
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Abstract Full text About this article

To address the compounded uncertainty in the observed output data, we introduce a new method of fuzzy regression modeling which is based on quadratic programming and fuzzy weights, so that the objective function represents the quadratic error for all of the central tendencies and the spreads. Also, the fuzzy weights are optimized for the fuzzy regression model estimation with crisp input and fuzzy output based on the adaptive fuzzy networks, considering symmetrical triangular fuzzy output. This paper aims to use the proposed method for the prediction of the output value in empirical applications where the observed value is a range or mean of several values, rather than a real fixed number. Two numerical examples were employed to demonstrate the efficiency of the method and compare the results of the proposed method with the previous ones such as linear programming (LP), quadratic programming (QP), as well as combination of linear programming and fuzzy weights (FWLP). The results show that the proposed method provides better prediction accuracy than other methods in surface roughness prediction of the grinding process.


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About this article

An Adaptive Fuzzy Inference System Model to Analyze Fuzzy Regression with Quadratic Programming and Fuzzy Weights Incorporating Uncertainty in the Observed Data

Show Author's information Mahdi Danesh1, Sedigheh Danesh2, Ali Maleki3( ), Tahereh Razzaghnia4
Department of Industrial, Mechanics, and Aerospace Engineering, Buein Zahra Technical University, Ghazvin 3451745346, Iran
Samaneh Yaran Danesh Electronic, Tabriz 51368, Iran
Department of Statistics, West Tehran Branch, Islamic Azad University, Tehran 15614, Iran
Department of Statistics, North Tehran Branch, Islamic Azad University, Tehran 15614, Iran

Abstract

To address the compounded uncertainty in the observed output data, we introduce a new method of fuzzy regression modeling which is based on quadratic programming and fuzzy weights, so that the objective function represents the quadratic error for all of the central tendencies and the spreads. Also, the fuzzy weights are optimized for the fuzzy regression model estimation with crisp input and fuzzy output based on the adaptive fuzzy networks, considering symmetrical triangular fuzzy output. This paper aims to use the proposed method for the prediction of the output value in empirical applications where the observed value is a range or mean of several values, rather than a real fixed number. Two numerical examples were employed to demonstrate the efficiency of the method and compare the results of the proposed method with the previous ones such as linear programming (LP), quadratic programming (QP), as well as combination of linear programming and fuzzy weights (FWLP). The results show that the proposed method provides better prediction accuracy than other methods in surface roughness prediction of the grinding process.

Keywords: surface roughness, linear programming, uncertainty, grinding, fuzzy regression, quadratic programming, adaptive fuzzy neural networks

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Received: 13 June 2021
Revised: 19 November 2022
Accepted: 12 February 2023
Published: 30 June 2023
Issue date: June 2023

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© The Author(s) 2023. Published by Tsinghua University Press.

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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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