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Fuzzy Regression Model Based on Incentre Distance and Application to Employee Performance Evaluation

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Abstract

Fuzzy regression model is developed to construct the relationship between independent variable and dependent variable in a fuzzy environment. In order to increase the explanatory performance of fuzzy regression model, the least-squares method usually is applied to determine the numeric coefficients based on the concept of distance. In this paper, we consider the fuzzy linear regression model with fuzzy input, fuzzy output, and crisp parameters, introduce a new distance based on the incentre point of triangular fuzzy number, merge least-squares method with the new incentre distance, and propose least-squares incentre distance (LSID) method. Finally, an example of employee job performance is given to illustrate the effectiveness and feasibility of the method. Comparisons with existing methods show that the calculation of LSID method is simpler on the premise of total estimation error satisfaction.

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Acknowledgements

The authors are very grateful to the editor and the anonymous reviewers for their constructive comments and suggestions that have led to an improved version of this paper. This paper is supported by the Humanities and Social Science Planning Fund of Ministry of Education of China (No. 18YJCZH036) and the Fundamental Research Fund for the Central University of China (No. 2015B28014).

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  1. School of Business Administration, Hohai University, Changzhou, China

    Yanbing Gong, Shuxin Yang, Hailiang Ma & Min Ge

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  1. Yanbing Gong
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  2. Shuxin Yang
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  4. Min Ge
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Correspondence to Yanbing Gong.

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Gong, Y., Yang, S., Ma, H. et al. Fuzzy Regression Model Based on Incentre Distance and Application to Employee Performance Evaluation. Int. J. Fuzzy Syst. 20, 2632–2639 (2018). https://doi.org/10.1007/s40815-018-0536-x

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  • DOI: https://doi.org/10.1007/s40815-018-0536-x

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