A new approach to formulate fuzzy regression models

Liang Hsuan Chen, Sheng Hsing Nien

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Article number105915
JournalApplied Soft Computing Journal
Publication statusPublished - 2020 Jan

All Science Journal Classification (ASJC) codes

  • Software


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