( 2304-8012 ) Intuitionistic fuzzy type basic uncertain information
نویسندگان
1 Department of Computer Science, University of Jaen, 23071-Jaen, Spain
2 Department of Computer Science, University of Jaen, 23071-Jaen, Spain
3 School of Automobile and Traffic Engineering, Hubei University of Arts and Sciences, Xiangyang, 441053, China; School of Business, Nanjing Normal University, Nanjing, China
4 Machine Intelligence Institute, Iona College, New Rochelle, NY
5 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
6 Department of Mathematics, Padima Janakalyan Banipith, Kukrakhupi, Jhargram, 721517, India
7 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
doi
10.22111/ijfs.2023.7840چکیده
Recently, a new paradigm for uncertain information has been proposed that can effectively handle various types ofuncertainty in decision-making problems. This approach utilizes a certainty degree, which is represented by a realnumber indicating the level of certainty associated with input values. However, just like intuitionistic fuzzy informationcan handle more problems that cannot be well modeled by fuzzy information, the certainty degree in basic uncertaininformation can also be intuitionistic fuzzy granule, which allows it to handle more uncertainty involved decision makingsituations. In this paper, we introduce the concept of intuitionistic fuzzy type basic uncertain information and explainits parameters. We also define a weighted arithmetic mean for aggregating this type of information and discuss differentapproaches for allocating induced weights based on trust preferred preference from four perspectives: (i) preference forhigher certainty degrees; (ii) aversion to higher levels of uncertainty; (iii) preference for greater differences in certaintydegrees; and (iv) preference for intuitionistic fuzzy certainties. Additionally, we explore trichotomic rules-based decisionmaking using intuitionistic fuzzy type basic uncertain information. Finally, we present an objective-subjective evaluationnumerical example utilizing these methods.
