( 2304-8012 ) Intuitionistic fuzzy type basic uncertain information

Document Type : Research Paper

Authors

1 School of Automobile and Traffic Engineering, Hubei University of Arts and Sciences, Xiangyang, 441053, China; School of Business, Nanjing Normal University, Nanjing, China

2 Machine Intelligence Institute, Iona College, New Rochelle, NY

3 Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, 441053, China; School of Automobile and Traffic Engineering, Hubei University of Arts and Sciences, Xiangyang, 441053, China

4 Department of Computer Science, University of Jaen, 23071-Jaen, Spain

5 Department of Mathematics, Padima Janakalyan Banipith, Kukrakhupi, Jhargram, 721517, India

6 Faculty of Civil Engineering, Slovak University of Technology, Radlinskho 11, Sk-810 05 Bratislava, Slovakia; Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, CE IT4Innovations, 30. dubna 22, 701 03 Ostrava, Czech Republic

Abstract

Recently, a new paradigm for uncertain information has been proposed that can effectively handle various types of
uncertainty in decision-making problems. This approach utilizes a certainty degree, which is represented by a real
number indicating the level of certainty associated with input values. However, just like intuitionistic fuzzy information
can handle more problems that cannot be well modeled by fuzzy information, the certainty degree in basic uncertain
information can also be intuitionistic fuzzy granule, which allows it to handle more uncertainty involved decision making
situations. In this paper, we introduce the concept of intuitionistic fuzzy type basic uncertain information and explain
its parameters. We also define a weighted arithmetic mean for aggregating this type of information and discuss different
approaches for allocating induced weights based on trust preferred preference from four perspectives: (i) preference for
higher certainty degrees; (ii) aversion to higher levels of uncertainty; (iii) preference for greater differences in certainty
degrees; and (iv) preference for intuitionistic fuzzy certainties. Additionally, we explore trichotomic rules-based decision
making using intuitionistic fuzzy type basic uncertain information. Finally, we present an objective-subjective evaluation
numerical example utilizing these methods.

Keywords

References

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Iranian Journal of Fuzzy Systems
Volume 20, Issue 5
September and October 2023
Pages 189-197

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