Adaptive weighted fuzzy interpolative reasoning based on representative values and similarity measures of interval type-2 fuzzy sets

Abstract

In this paper, we propose an adaptive weighted fuzzy interpolative reasoning (AWFIR) method based on representative values (RVs) and similarity measures of interval type-2 polygonal fuzzy sets (IT2PFSs). The proposed AWFIR method can overcome the shortcomings of Cheng et al.’s adaptive fuzzy interpolative reasoning (AFIR) method (2016), Chen and Adam's AFIR method (2018), Yang and Shen's AFIR method (2011) and Yang et al.’s AFIR method (2017) to deal with the diarrheal disease prediction problem. The advantages of the proposed AWFIR method are (1) it produces higher degrees of consistency between the FIR results and (2) it deals with AWFIR using IT2PFSs rather than type-1 fuzzy sets (T1FSs).