A new version of 2-tuple fuzzy linguistic representation model for computing with words

By Jin-Hsien Wang, Jongyun Hao. IEEE transactions on fuzzy systems, 2006

483 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/52f942e56fce1e63186585bfca79bdf051cf80bb

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Citations in the corpus

Probabilistic linguistic term sets in multi-attribute group decision making (2016, background): “Wang and Hao [27] considered two proportional linguistic terms at first.”
Computing the Numerical Scale of the Linguistic Term Set for the 2-Tuple Fuzzy Linguistic Representation Model (2009, background): “Wang and Hao [42] provided a new (proportional) 2-tuple fuzzy linguistic representation model for CW.”
Some Hesitant Fuzzy Aggregation Operators with Their Application in Group Decision Making (2011, background): “…aggregation (Fodor et al. 1995) based on quasi-arithmetic means (Hardy et al. 1934) generalizes a wide range of aggregation operators, and has been extended to aggregate many kinds of fuzzy information (Wang and Hao 2006; Merigó and Gil-Lafuente 2009; Xu and Xia 2011c; Merigó and Casanovas 2011).”
Computing with Words in Decision support Systems: An overview on Models and Applications (2010, methodology): “…for symbolic information to proportional 2-tuple and underlying deﬁni-tions of linguistic labels and linguistic variables are taken into account in the process of aggregating linguistic information by assigning canonical characteristic values of the corresponding linguistic labels 89 , 90 .”
Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making (2015, methodology): “the proportional 2-tuple model [14], and so on.”
Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets (2015, background): “Many different models were proposed to represent and calculate the values of a linguistic variable, such as the semantic model [3], the virtual linguist model [29], the 2-tuple linguistic model [30], and the proportional 2-tuple linguistic model [31].”
Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making (2017, methodology): “Different linguistic computational models have been developed for CW [6], [7], such as models based on fuzzy membership functions [8], symbolic models based on ordinal scales [9], models based on type-2 fuzzy sets [10], the 2-tuple linguistic model [11], [12], the proportional 2-tuple linguistic mod”
An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges (2012, methodology): “Wang and Hao [114] introduced the proportional 2-tuple, that is a new way to represent and operate with the linguistic information, being a generalization and extension of 2-tuple linguistic representation model [44].”
A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets (2013, methodology): “The use of hesitant fuzzy linguistic term sets (HFLTS) [33] improves the previous linguistic approaches [20,37,40] in their aim of achieving the previous improvements pointed out.”
An Adaptive Consensus Support Model for Group Decision-Making Problems in a Multigranular Fuzzy Linguistic Context (2009, methodology): “This approach has been successfully applied to different problems [2], [3], [6], [20], [22], [29], [30], [34], [36], [43], [56], [63].”
A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making (2014, background): “Nevertheless, in the literature, different authors [16, 26, 29] point out the necessity of richer expressions, mainly for decision making problems with high degrees of uncertainty, in which experts might hesitate among different linguistic terms to express their preferences.”
Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach (2008, background): “, [1], [4], [9], [12], [26], [27], [29], [30], and [34]–[38]; however, as claimed in [12]–[15] and [17], “Words mean different things to different people, and so are uncertain.”