On Generalized Correlation Coefficients of Picture Fuzzy Sets With Their Applications

2021 ◽  
Vol 10 (2) ◽  
pp. 59-81
Author(s):  
Surender Singh ◽  
Abdul Haseeb Ganie ◽  
Sumita Lalotra
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Crucial Role ◽  
Correlation Coefficients ◽  
Intuitionistic Fuzzy Sets ◽  
Numerical Examples ◽  
Multi Attribute Decision Making ◽  
Picture Fuzzy Sets ◽  
Generalized Correlation

Picture fuzzy sets (PFSs) play a crucial role in uncertain/vague environments than intuitionistic fuzzy sets (IFSs) which do not take into consideration the degree of neutrality of an element. In this paper, the authors have proposed generalized correlation coefficients of PFSs along with some properties. The effectiveness and application of the proposed generalized correlation coefficients of PFSs in pattern recognition and multi-attribute decision making (MADM) is also discussed with the help of numerical examples.

New generalized intuitionistic fuzzy divergence measure with applications to multi-attribute decision making and pattern recognition

2020 ◽  
Vol 13 ◽  
Author(s):  
Adeeba Umar ◽  
Ram Naresh Saraswat
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
The Other ◽  
Divergence Measure ◽  
Intuitionistic Fuzzy ◽  
Multi Attribute Decision Making ◽  
Prime Objective ◽  
Fuzzy Divergence

Background: The notion of fuzzy set was introduced by Zadeh. After that, many researchers extended the concept of fuzzy sets in different ways. Atanassov introduced the concept of intuitionistic fuzzy sets as an extension of fuzzy sets. This concept is applied in many fields such as bio-informatics, image processing, decision making, feature selection, pattern recognition etc. Objectives: The prime objective of this paper is to introduce a new generalized intuitionistic fuzzy divergence measure with proof of its validity and discussions on its elegant properties. Applications of the proposed divergence measure in multi-attribute decision making and pattern recognition are also discussed with some numerical illustrations. Further, the proposed divergence measure is compared with other methods for solving MADM and pattern recognition problems which exist in the literature. Methods: Divergence measure method is used to measure the divergence between two given sets. Also, the results of the other existing measures are also given to compare with the proposed measure. Results: We see that our proposed divergence measure found much better results in comparison with the other existing methods. Conclusion: A new divergence measure for intuitionistic fuzzy sets is introduced with some of its properties. Applications of the proposed divergence measure to pattern recognition and MADM are illustrated through examples. The comparison of the proposed method with the existing methods shows the legacy of the results of the proposed method. It is concluded that the proposed divergence measure is effective for solving real world problems related to MADM and pattern recognition.

scholarly journals NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS AND APPLICATIONS

2018 ◽  
Vol 34 (3) ◽  
pp. 219-231 ◽  
Author(s):  
Nhung Thi Le ◽  
Dinh Van Nguyen ◽  
Chau Minh Ngoc ◽  
Thao Xuan Nguyen
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Supplier Selection ◽  
Intuitionistic Fuzzy Sets ◽  
Multi Criteria Decision Making ◽  
Dissimilarity Measures ◽  
Selection Problems ◽  
Mcdm Method ◽  
Picture Fuzzy Sets

The dissimilarity measures between fuzzy sets/intuitionistic fuzzy sets/picture fuzzy sets are studied and applied in various matters. In this paper, we propose some new dissimilarity measures on picture fuzzy sets. This new dissimilarity measures overcome the restrictions of all existing dissimilarity measures on picture fuzzy sets. After that, we apply these new measures to the pattern recognition problems. Finally, we introduce a multi-criteria decision making (MCDM) method that used the new dissimilarity measures and apply them in the supplier selection problems.

scholarly journals Covering-Based Spherical Fuzzy Rough Set Model Hybrid with TOPSIS for Multi-Attribute Decision-Making

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 547 ◽  
Author(s):  
Shouzhen Zeng ◽  
Azmat Hussain ◽  
Tahir Mahmood ◽  
Muhammad Irfan Ali ◽  
Shahzaib Ashraf ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Rough Set ◽  
Real Life ◽  
Intuitionistic Fuzzy Sets ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy ◽  
Multi Attribute Decision Making ◽  
Topsis Technique ◽  
Picture Fuzzy Sets

In real life, human opinion cannot be limited to yes or no situations as shown in an ordinary fuzzy sets and intuitionistic fuzzy sets but it may be yes, abstain, no, and refusal as treated in Picture fuzzy sets or in Spherical fuzzy (SF) sets. In this article, we developed a comprehensive model to tackle decision-making problems, where strong points of view are in the favour; neutral; and against some projects, entities, or plans. Therefore, a new approach of covering-based spherical fuzzy rough set (CSFRS) models by means of spherical fuzzy β -neighborhoods (SF β -neighborhoods) is adopted to hybrid spherical fuzzy sets with notions of covering the rough set. Then, by using the principle of TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to present the spherical fuzzy, the TOPSIS approach is presented through CSFRS models by means of SF β -neighborhoods. Via the SF-TOPSIS methodology, a multi-attribute decision-making problem is developed in an SF environment. This model has stronger capabilities than intuitionistic fuzzy sets and picture fuzzy sets to manage the vague and uncertainty. Finally, the proposed method is demonstrated through an example of how the proposed method helps us in decision-making problems.

scholarly journals T-Spherical Fuzzy Einstein Hybrid Aggregation Operators and Their Applications in Multi-Attribute Decision Making Problems

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 365 ◽  
Author(s):  
Muhammad Munir ◽  
Humaira Kalsoom ◽  
Kifayat Ullah ◽  
Tahir Mahmood ◽  
Yu-Ming Chu
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Real Life ◽  
Intuitionistic Fuzzy Sets ◽  
Aggregation Operators ◽  
Multi Attribute Decision Making ◽  
Algebraic Operations ◽  
Einstein Operations ◽  
Picture Fuzzy Sets ◽  
Einstein Product

T-spherical fuzzy set is a recently developed model that copes with imprecise and uncertain events of real-life with the help of four functions having no restrictions. This article’s aim is to define some improved algebraic operations for T-SFSs known as Einstein sum, Einstein product and Einstein scalar multiplication based on Einstein t-norms and t-conorms. Then some geometric and averaging aggregation operators have been established based on defined Einstein operations. The validity of the defined aggregation operators has been investigated thoroughly. The multi-attribute decision-making method is described in the environment of T-SFSs and is supported by a comprehensive numerical example using the proposed Einstein aggregation tools. As consequences of the defined aggregation operators, the same concept of Einstein aggregation operators has been proposed for q-rung orthopair fuzzy sets, spherical fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and intuitionistic fuzzy sets. To signify the importance of proposed operators, a comparative analysis of proposed and existing studies is developed, and the results are analyzed numerically. The advantages of the proposed study are demonstrated numerically over the existing literature with the help of examples.

scholarly journals Algorithm for T-Spherical Fuzzy Multi-Attribute Decision Making Based on Improved Interactive Aggregation Operators

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 670 ◽  
Author(s):  
Harish Garg ◽  
Muhammad Munir ◽  
Kifayat Ullah ◽  
Tahir Mahmood ◽  
Naeem Jan
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Aggregation Operators ◽  
Neutrosophic Sets ◽  
Counter Example ◽  
Operational Laws ◽  
Special Cases ◽  
Multi Attribute Decision Making ◽  
Picture Fuzzy Sets

The objective of this manuscript is to present some new, improved aggregation operators for the T-spherical fuzzy sets, which is an extension of the several existing sets, such as intuitionistic fuzzy sets, picture fuzzy sets, neutrosophic sets, and Pythagorean fuzzy sets. In it, some new, improved operational laws and their corresponding properties are studied. Further, based on these laws, we propose some geometric aggregation operators and study their various relationships. Desirable properties, as well as some special cases of the proposed operators, are studied. Then, based on these proposed operators, we present a decision-making approach to solve the multi-attribute decision-making problems. The reliability of the presented decision-making method is explored with the help of a numerical example and the proposed results are compared with several prevailing studies’ results. Finally, the superiority of the proposed approach is explained with a counter example to show the advantages of the proposed work.

scholarly journals A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 999 ◽  
Author(s):  
Jin ◽  
Wu ◽  
Sun ◽  
Zeng ◽  
Luo ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Correlation Coefficient ◽  
Ideal Point ◽  
New Correlation ◽  
Fuzzy Correlation ◽  
Relative Proximity ◽  
Fuzzy Madm ◽  
Multi Attribute Decision Making ◽  
Picture Fuzzy Sets

As a generalization of several fuzzy tools, picture fuzzy sets (PFSs) hold a special ability to perfectly portray inherent uncertain and vague decision preferences. The intention of this paper is to present a Pearson’s picture fuzzy correlation-based model for multi-attribute decision-making (MADM) analysis. To this end, we develop a new correlation coefficient for picture fuzzy sets, based on which a Pearson’s picture fuzzy closeness index is introduced to simultaneously calculate the relative proximity to the positive ideal point and the relative distance from the negative ideal point. On the basis of the presented concepts, a Pearson’s correlation-based model is further presented to address picture fuzzy MADM problems. Finally, an illustrative example is provided to examine the usefulness and feasibility of the proposed methodology.

scholarly journals A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Zhenhua Zhang ◽  
Min Wang ◽  
Yong Hu ◽  
Jingyu Yang ◽  
Youpei Ye ◽  
...  
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Recognition Accuracy ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Intuitionistic Fuzzy ◽  
Simulation Results ◽  
Interval Valued

We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS), which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS) and interval-valued IFS (IVIFS). And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.

Sign in / Sign up

Export Citation Format

Share Document