Approach for Establishing Intuitionistic Fuzzy Linear Regression Models Based on Weakest T-Norm Arithmetic

Liang Hsuan Chen, Sheng Hsing Nien

研究成果: Article同行評審

摘要

This article establishes an intuitionistic fuzzy linear regression model (IFLRM) under the consideration that the explanatory and response variables in the observation data set as well as the parameters of the model are intuitionistic fuzzy numbers (IFNs). The weakest T-norm arithmetic is applied in the formulation of the IFLRMs to avoid wide spreads in the predicted IFN responses. The sign of the parameters is determined in the formulation process. We propose a mathematical programming problem to find the optimal IFN parameters. The goal of the optimization is to minimize the absolute distances between the observed and predicted IFNs. To enhance computational efficiency, a three-step procedure is proposed for solving a mathematical programming problem when the number of explanatory variables or the size of the observation data set is large. Comparisons with existing approaches indicate that the proposed approach has outstanding performance in terms of similarity and distance measures.

原文English
文章編號9026818
頁(從 - 到)1431-1445
頁數15
期刊IEEE Transactions on Fuzzy Systems
29
發行號6
DOIs
出版狀態Published - 2021 六月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 計算機理論與數學
  • 人工智慧
  • 應用數學

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