Computing the Numerical Scale of the Linguistic Term Set for the 2-Tuple Fuzzy Linguistic Representation Model

2009 ◽  
Vol 17 (6) ◽  
pp. 1366-1378 ◽  
Author(s):  
Yucheng Dong ◽  
Yinfeng Xu ◽  
Shui Yu
Keyword(s):  
Linguistic Representation ◽  
Numerical Scale ◽  
Representation Model ◽  
Linguistic Term ◽  
Fuzzy Linguistic

AN APPROACH FOR COMBINING LINGUISTIC AND NUMERICAL INFORMATION BASED ON THE 2-TUPLE FUZZY LINGUISTIC REPRESENTATION MODEL IN DECISION-MAKING

2000 ◽  
Vol 08 (05) ◽  
pp. 539-562 ◽  
Author(s):  
F. HERRERA ◽  
L. MARTINEZ
Keyword(s):  
Decision Making ◽  
Linguistic Information ◽  
Loss Of Information ◽  
Linguistic Representation ◽  
Representation Model ◽  
Decision Making Problem ◽  
Linguistic Term ◽  
Multi Attribute Decision Making ◽  
Fuzzy Linguistic ◽  
Transformation Processes

In this paper we shall develop a procedure for combining numerical and linguistic information without loss of information in the transformation processes between numerical and linguistic information, taking as base for representing the information the 2-tuple fuzzy linguistic representation model. We shall analyze the conditions to impose the linguistic term set in order to ensure that the combination procedure does not produce any loss of information. Afterwards the aggregation process will be applied to a decision procedure over a multi-attribute decision-making problem dealing with numerical and linguistic information, that is, with qualitative and quantitative attributes.

Multi-Criteria Decision-Making Method Based on a Weighted 2-Tuple Fuzzy Linguistic Representation Model

2021 ◽  
pp. 1-16
Author(s):  
Jia-Wen Wang
Keyword(s):  
Decision Making ◽  
Feature Selection Method ◽  
Weighted Average ◽  
Numerical Calculations ◽  
Multi Criteria Decision Making ◽  
Linguistic Representation ◽  
Representation Model ◽  
Priority Ranking ◽  
Human Thinking ◽  
Fuzzy Linguistic

Multi-criteria decision-making (MCDM) methods are used in the selection and evaluation of alternatives. However, too many decision criteria and numerical calculations will increase the computational complexity and make the calculation process difficult to understand. In this paper, a weighted 2-tuple fuzzy linguistic representation model is proposed. The contributions of this study are as follows: (1) Feature selection method was used to remove the redundant or irrelevant feature attributes, thereby simplifying calculations and reducing calculation complexities. (2) The integration of the 2-tuple linguistic representation model simplifies the complexity of numerical calculations. The calculation of qualitative scales can be closer to the human thinking model, and loss of information can be avoided during calculations through the appropriate model. (3) Information fusion technology, i.e., ordered weighted average operator (OWA), was used. The method simplifies the traditional OWA calculation and can be calculated according to the priority order of the indicators. (4) Four major shareholding companies in Taiwan 50 ETF stocks were selected as experimental cases. In total, 992 tuples were obtained and 29 technical indicators were analyzed. The results indicate that case A1 is the most stable among the four stocks considered under different decision-making situations, and it has the first priority ranking.

Sign in / Sign up

Export Citation Format

Share Document