| Autor: |
Le-Sheng Jin, Ya-Qiang Xu, Zhen-Song Chen, Radko Mesiar, Ronald R. Yager |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-7 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1875-6883 |
| DOI: |
10.1007/s44196-022-00066-9 |
| Popis: |
Abstract Basic uncertain information is a newly proposed normative formulation to express and model uncertain information. This study further generalizes this concept by introducing the concept of refined interval of discourse in which the true value is known to be included. Hence, we define some new definitions of relative basic uncertain information, relative certainty/uncertainty degree and comprehensive certainty/uncertainty with some related measurements and analysis. With the introduced uncertain data type, we define two corresponding aggregation operators, namely, the relative basic uncertain information valued weighted arithmetic mean operator and the interval-induced relative basic uncertain information valued ordered weight averaging operator. An application of the proposed concepts and methods in multi-agents evaluation is provided. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink