A new similarity/distance measure between intuitionistic fuzzy sets based on the transformed isosceles triangles and its applications to pattern recognition

2019 ◽  
Vol 116 ◽  
pp. 439-453 ◽  
Author(s):  
Qian Jiang ◽  
Xin Jin ◽  
Shin-Jye Lee ◽  
Shaowen Yao
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Sets ◽  
Intuitionistic Fuzzy ◽  
Similarity Distance ◽  
Isosceles Triangles

A Dynamic Fuzzy Sets Method and its Application to Pattern Recognition

2012 ◽  
Vol 263-266 ◽  
pp. 2602-2605
Author(s):  
Zhen Hua Zhang ◽  
Yong Hu ◽  
Xiao Hua Ke
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Sets ◽  
Experimental Results ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

We present a novel dynamic fuzzy sets (DFS) method, which is the generalization of fuzzy sets (FS) and the dynamization of interval-valued intuitionistic fuzzy sets (IVIFS). First, by analyzing the degree of hesitancy, we propose a DFS model from IVIFS. Second, we introduce the distance measure of DFS. Finally, a pattern recognition example is given to demonstrate the application of DFS, and the experimental results show that the DFS method is more effective than some IVIFS methods.

Width-based distance measures on interval-valued intuitionistic fuzzy sets

2021 ◽  
pp. 1-13
Author(s):  
Xi Li ◽  
Chunfeng Suo ◽  
Yongming Li
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Intuitionistic Fuzzy ◽  
Interval Valued ◽  
Dissimilarity Function ◽  
Better Than

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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