|تعداد مشاهده مقاله||9,882,506|
|تعداد دریافت فایل اصل مقاله||6,471,926|
( 2304-8012 ) Intuitionistic fuzzy type basic uncertain information
|Iranian Journal of Fuzzy Systems|
|مقاله 13، دوره 20، شماره 5، آذر و دی 2023، صفحه 189-197 اصل مقاله (168.83 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22111/ijfs.2023.7840|
|L. S. Jin1؛ R. R. Yager2؛ C. Ma* 3؛ L. M. Lopez4؛ R. M. Rodrguez4؛ T. Senapati5؛ R. Mesiar6|
|1School of Automobile and Traffic Engineering, Hubei University of Arts and Sciences, Xiangyang, 441053, China; School of Business, Nanjing Normal University, Nanjing, China|
|2Machine Intelligence Institute, Iona College, New Rochelle, NY|
|3Hubei 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|
|4Department of Computer Science, University of Jaen, 23071-Jaen, Spain|
|5Department of Mathematics, Padima Janakalyan Banipith, Kukrakhupi, Jhargram, 721517, India|
|6Faculty 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|
|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.
|Aggregation operator؛ basic uncertain information؛ information fusion؛ intuitionistic fuzzy type basic uncertain information؛ preference involved evaluation؛ rules-based decision making|
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