Adaptive fuzzy interpolative reasoning based on similarity measures of polygonal fuzzy sets and novel move and transformation techniques

被引:6
|
作者
Chen, Shyi-Ming [1 ]
Barman, Dipto [1 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan
关键词
AFIR; FIR; Move and transformation techniques; Polygonal fuzzy sets; Ranking values; RANKING VALUES; RULE INTERPOLATION; REPRESENTATIVE VALUES; LOGICAL RELATIONSHIPS; TIME-SERIES; NUMBERS; SCALE;
D O I
10.1016/j.ins.2019.03.034
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose a new adaptive fuzzy interpolative reasoning (AFIR) method based on similarity measures of polygonal fuzzy sets (PFSs) and novel move and transformation techniques for solving contradictory fuzzy interpolative reasoning (FIR) results that occur at FIR components. The proposed AFIR method is applied for dealing with the diarrhea) disease prediction problem. The experimental results show that the proposed AFIR method obtains a higher consistency degree between the FIR results compared to the ones obtained by Chen and Adam's AFIR method (2017), Chen and Adam's AFIR method (2018), Chen and Barman's AFIR method (2019), Cheng et al.'s AFIR method (2016), Yang et al.'s AFIR method (2017) and Yang and Shen's AFIR method (2011). (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:303 / 315
页数:13
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