INTUITIONISTIC FUZZY LINGUISTIC QUANTIFIERS BASED ON INTUITIONISTIC FUZZY-VALUED FUZZY MEASURES AND INTEGRALS

2009 ◽  
Vol 17 (03) ◽  
pp. 427-448 ◽  
Author(s):  
LICONG CUI ◽  
YONGMING LI ◽  
XIAOHONG ZHANG
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Integral ◽  
Fuzzy Measures ◽  
Intuitionistic Fuzzy ◽  
Norm Form ◽  
Form Theorem ◽  
Linguistic Quantifier ◽  
Fuzzy Linguistic Quantifiers ◽  
Linguistic Quantifiers ◽  
Fuzzy Linguistic

In this paper, we generalize Ying's model of linguistic quantifiers [M.S. Ying, Linguistic quantifiers modeled by Sugeno integrals, Artificial Intelligence, 170 (2006) 581-606] to intuitionistic linguistic quantifiers. An intuitionistic linguistic quantifier is represented by a family of intuitionistic fuzzy-valued fuzzy measures and the intuitionistic truth value (the degrees of satisfaction and non-satisfaction) of a quantified proposition is calculated by using intuitionistic fuzzy-valued fuzzy integral. Description of a quantifier by intuitionistic fuzzy-valued fuzzy measures allows us to take into account differences in understanding the meaning of the quantifier by different persons. If the intuitionistic fuzzy linguistic quantifiers are taken to be linguistic fuzzy quantifiers, then our model reduces to Ying's model. Some excellent logical properties of intuitionistic linguistic quantifiers are obtained including a prenex norm form theorem. A simple example is presented to illustrate the use of intuitionistic linguistic quantifiers.

scholarly journals Medical Pattern Recognition: Applying an Improved Intuitionistic Fuzzy Cross-Entropy Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Kuo-Chen Hung
Keyword(s):  
Pattern Recognition ◽  
Medical Diagnosis ◽  
Entropy Measure ◽  
Intuitionistic Fuzzy ◽  
Hesitation Degree ◽  
Computer Based ◽  
Fuzzy Linguistic Quantifiers ◽  
Linguistic Quantifiers ◽  
Fuzzy Linguistic ◽  
Mathematical Connection

One of the toughest challenges in medical diagnosis is the handling of uncertainty. Since medical diagnosis with respect to the symptoms uncertain, they will be assumed to have an intuitive nature. Thus, to obtain the uncertain optimism degree of the doctor, fuzzy linguistic quantifiers will be used. The aim of this article is to provide an improved nonprobabilistic entropy approach to support doctors examining the work of the preliminary diagnosing. The proposed entropy measure is based on intuitionistic fuzzy sets, extrainformation regarding hesitation degree, and an intuitive and mathematical connection between the notions of entropy in terms of fuzziness and intuitionism has been revealed. An illustrative example for medical pattern recognition demonstrates the usefulness of this study. Furthermore, in order to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed to support doctors in making more efficient judgments.

POINT MATCHING WITH A "SOFT" RELAXATION ALGORITHM

2000 ◽  
Vol 08 (04) ◽  
pp. 481-493
Author(s):  
OREN ROTBARD ◽  
H. B. MITCHELL ◽  
D. D. ESTRAKH
Keyword(s):  
Signal Processing ◽  
Computational Complexity ◽  
Brute Force ◽  
Point Matching ◽  
Relaxation Algorithm ◽  
Moderate Number ◽  
Fuzzy Linguistic Quantifiers ◽  
Linguistic Quantifiers ◽  
Fuzzy Linguistic ◽  
High Computational Complexity

Point-matching involves the matching of pairs of points from two sets of partially correlated points. It is an important task which is used in many different areas of signal processing. Although it is possible to perform point-matching using a brute-force algorithm, the high computational complexity makes it unfeasible even for a moderate number of points. In these circumstances an iterative relaxation algorithm is widely used. The traditional relaxation algorithm works well as long as the number of points in one set which do not have a corresponding pair in the second set is small and the positions of all the points are accurately known. When these conditions do not hold, the performance of the relaxation algorithm is substantially reduced. In this paper we formulate a "soft" relaxation algorithm using the concept of fuzzy linguistic quantifiers. The performance of the new relaxation algorithm is found to consistently exceed that of the traditional relaxation algorithm.

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