JOURNAL ARTICLE
Arithmetic Operations on Generalized Trapezoidal Hesitant Fuzzy Numbers and Their Application to Solving Generalized Trapezoidal Hesitant Fully Fuzzy Equation.
Published In: International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 2024, v. 32, n. 1. P. 85 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Babakordi, F. 3 of 3
Abstract
Algebraic operations on generalized hesitant fuzzy numbers are key tools to address the problems with decision uncertainty. In this paper, by studying the arithmetic operations on generalized trapezoidal hesitant fuzzy numbers, modified arithmetic operations are introduced for this class of numbers so that, using these arithmetic operations, the multiplication and division of two generalized trapezoidal hesitant fuzzy numbers are always generalized trapezoidal hesitant fuzzy numbers. Furthermore, a generalized trapezoidal hesitant fuzzy number raised to the power of a real number is a generalized trapezoidal hesitant fuzzy number, and in the defined division, the case where the denominator becomes zero is not considered. Numerical examples are used to show the shortcomings of the previous arithmetic operations as well as the efficiencies of the arithmetic operations proposed in this research for generalized trapezoidal hesitant fuzzy numbers. Finally, the application of the proposed new arithmetic operations to generalized trapezoidal hesitant fuzzy numbers in solving the generalized trapezoidal hesitant fully fuzzy equation is discussed. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. 2024/02, Vol. 32, Issue 1, p85
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2024
- ISSN:0218-4885
- DOI:10.1142/S0218488524500041
- Accession Number:175549741
- Copyright Statement:Copyright of International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.