A new approach to formulate fuzzy regression models

Liang Hsuan Chen, Sheng Hsing Nien

研究成果: Article

摘要

A fuzzy regression model is developed to construct the relationship between the response and explanatory variables in fuzzy environments. To enhance explanatory power and take into account the uncertainty of the formulated model and parameters, a new operator, called the fuzzy product core (FPC), is proposed for the formulation processes to establish fuzzy regression models with fuzzy parameters using fuzzy observations that include fuzzy response and explanatory variables. In addition, the sign of parameters can be determined in the model-building processes. Compared to existing approaches, the proposed approach reduces the amount of unnecessary or unimportant information arising from fuzzy observations and determines the sign of parameters in the models to increase model performance. This improves the weakness of the relevant approaches in which the parameters in the models are fuzzy and must be predetermined in the formulation processes. The proposed approach outperforms existing models in terms of distance, mean similarity, and credibility measures, even when crisp explanatory variables are used.

原文English
文章編號105915
期刊Applied Soft Computing Journal
86
DOIs
出版狀態Published - 2020 一月

指紋

Uncertainty

All Science Journal Classification (ASJC) codes

  • Software

引用此文

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A new approach to formulate fuzzy regression models. / Chen, Liang Hsuan; Nien, Sheng Hsing.

於: Applied Soft Computing Journal, 卷 86, 105915, 01.2020.

研究成果: Article

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