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A new linguistic computational model based on discrete fuzzy numbers for computing with words

Authors:

Sebastia Massanet,

Juan Vicente Riera,

Enrique Herrera-ViedmaAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 258

Pages 277 - 290

https://doi.org/10.1016/j.ins.2013.06.055

Published: 01 February 2014 Publication History

Abstract

In recent years, several different linguistic computational models for dealing with linguistic information in processes of computing with words have been proposed. However, until now all of them rely on the special semantics of the linguistic terms, usually fuzzy numbers in the unit interval, and the linguistic aggregation operators are based on aggregation operators in [0,1]. In this paper, a linguistic computational model based on discrete fuzzy numbers whose support is a subset of consecutive natural numbers is presented ensuring the accuracy and consistency of the model. In this framework, no underlying membership functions are needed and several aggregation operators defined on the set of all discrete fuzzy numbers are presented. These aggregation operators are constructed from aggregation operators defined on a finite chain in accordance with the granularity of the linguistic term set. Finally, an example of a multi-expert decision-making problem in a hierarchical multi-granular linguistic context is given to illustrate the applicability of the proposed method and its advantages.

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 258, Issue

February, 2014

463 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2013.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 February 2014

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https://dl.acm.org/doi/10.1007/s00500-023-09371-7
Munar-Covas MMassanet SRuiz-Aguilera D(2023)Why are Discrete Implications Necessary? An Analysis Through the Discretization ProcessIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.320445031:5(1484-1496)Online publication date: 1-May-2023
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Su HWu QTang XHuang T(2022)A consistency and consensus-driven approach for granulating linguistic information in GDM with distributed linguistic preference relationsArtificial Intelligence Review10.1007/s10462-022-10344-956:7(6627-6659)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/s10462-022-10344-9
Yan LPei Z(2022)A novel linguistic decision-making method based on the voting model for large-scale linguistic decision makingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06382-026:2(787-806)Online publication date: 1-Jan-2022
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Cheng YLi YYang J(2021)Multi-attribute decision-making method based on a novel distance measure of linguistic intuitionistic fuzzy setsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20142940:1(1147-1160)Online publication date: 1-Jan-2021
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