Interval Cut-Set of Generalized Interval-Valued Intuitionistic Fuzzy Sets

2012 ◽  
Vol 2 (3) ◽  
pp. 35-50 ◽  
Author(s):  
Amal Kumar Adak ◽  
Monoranjan Bhowmik ◽  
Madhumangal Pal

In this paper, some different types of interval cut-set of genaralized interval-valued intuitionistic fuzzy sets (GIVIFSs), complement of these cut-sets are introduced. Some properties of those cut-set of GIVIFSs are investigated. Also three decomposition theorems of GIVIFSs are obtained based on the different cut-set of GIVIFSs. These works can also be used in setting up the basic theory of GIVIFSs.

Author(s):  
Amal Kumar Adak ◽  
Monoranjan Bhowmik ◽  
Madhumangal Pal

In this chapter, the authors establish decomposition theorems of Generalized Interval-Valued Intuitionistic Fuzzy Sets (GIVIFS) by use of cut sets of generalized interval-valued intuitionistic fuzzy sets. First, new definitions of eight kinds of cut sets generalized interval-valued intuitionistic fuzzy sets are introduced. Second, based on these new cut sets, the decomposition generalized interval-valued intuitionistic fuzzy sets are established. The authors show that each kind of cut sets corresponds to two kinds of decomposition theorems. These results provide a fundamental theory for the research of generalized interval-valued intuitionistic fuzzy sets.


2021 ◽  
pp. 1-13
Author(s):  
Xi Li ◽  
Chunfeng Suo ◽  
Yongming Li

An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures.


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