Cited by 12,187Fuzzy Logic and Control SystemsAdvanced Algebra and LogicMathematical Control Systems and Analysis

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The concept of a linguistic variable and its application to approximate reasoning—I

Zadeh, L.A.·1975·Information Sciences

DOI 10.1016/0020-0255(75)90036-5

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“…stakeholder perceptions in terms of leader effectiveness, and that the cognitive processes through which stakeholders evaluate leaders can be seen as “strongly influenced by human judgment, perceptions, and emotions” rather than hyper-ratio-nal (Zadeh, 1975, p. 200), this is a surprising omission.”
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“The four theories discussed here that specialize in solving these types of problems include the fuzzy set theory Zadeh [14], interval mathematics [15], the probability set theory [16], and the rough set theory Pawlak [17].”
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Background
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“Combining the hesitating fuzzy sets [21] and LTSs [22], Rodriguez, Martinez and Herrera [23] devised the hesitant fuzzy LTSs (HFLTSs).”
Retracted
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“To overcome this issue, Zadeh proposed the extension of fuzzy sets with fuzzy membership values (secondary membership values) known as type-2 fuzzy sets [84].”
Method
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“The concept of fuzzy logic has proved to be very useful when dealing with uncertain or indistinct information.(31) The block diagram explaining the mapping of the input–output characteristics of the wear behavior of the Al MMCs using Mamdani-based fuzzy logic controller (FLC) is shown in Supplemental Fig.”
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“In the literature there are various extensions of ordinary fuzzy sets that are type-2 fuzzy sets (T2FS) [5], intuitionistic fuzzy sets (IFS) [6], hesitant fuzzy sets (HFS) [7], and neutrosophic sets (NS) [8].”
Retracted
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“intuitionistic fuzzy set (Atanassov 1986), type-2 fuzzy set (Zadeh 1975), vague set (Bustince and Burillo 1996), interval-valued fuzzy set (Grzegorzewski 2004), etc.”
Background
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“Therefore, various linguistic variables have been proposed to express experts’ uncertain evaluations for avoiding information loss [15], which also have been introduced for supplier selection problems.”
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Retracted
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“Definition 2.1 [50] Let t and t + 1 denote the odd number and cardinality of a set.”
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“Zadeh [10] originally put forward the concept of linguistic variables to describe the assessment information qualitatively.”
Background
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“Therefore, Zadeh created the theory of type-2 fuzzy logic systems (T2FSs) in 1975 [17].”
Background
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Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: The case of TripAdvisor
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“51 A type-2 fuzzy set is characterized by presenting several membership functions; that is, “ its membership has membership.”
An Improved FMEA Method Based on ANP with Probabilistic Linguistic Term Sets
Zhang, Z.-X., Yang, L., Cao, Y., & Xu, Y.-W. (2022) · International Journal of Fuzzy Systems · doi:10.1007/s40815-022-01302-2 · Cited by 22
“The concept of linguistic variable is first introduced by Zadeh [50] in 1975 to model the linguistic expressions verbalized by decisionmakers.”
Background
2DLIF-PROMETHEE based on the hybrid distance of 2-dimension linguistic intuitionistic fuzzy sets for multiple attribute decision making
Zhu, H., & Zhao, J. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.117219 · Cited by 22
Selection of most effective COVID-19 virus protector using a novel MCGDM technique under linguistic generalised spherical fuzzy environment
Haque, T. S., Alam, S., & Chakraborty, A. (2022) · Computational and Applied Mathematics · doi:10.1007/s40314-022-01776-8 · Cited by 22
“For this reason a new concept, namely, linguistic variables (LVs) (Zadeh 1975) have been recognized to grab the information which cannot be approximated by accurate numbers.”
Background
AI adoption in the hiring process – important criteria and extent of AI adoption
Bhatt, P. (2022) · foresight · doi:10.1108/fs-07-2021-0144 · Cited by 33
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“developed type-n fuzzy sets to handle membership function uncertainty [51].”
Background
Fuzzy Logical Algebra and Study of the Effectiveness of Medications for COVID-19
Khalil, S., Hassan, A., Alaskar, H., Khan, W., & Hussain, A. (2021) · Mathematics · doi:10.3390/math9222838 · Cited by 22
Designing DEMATEL method under bipolar fuzzy environment
Deva, N., & Felix, A. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-211112 · Cited by 22
“Zadeh [24] derived fuzzy relation, fuzzy algorithms, fuzzy turing machines and some properties of fuzzy sets and estimated the reasoning using linguistic variables [26].”
A new type of dyad fuzzy β-covering rough set models base on fuzzy information system and its practical application
Niu, X.-L., Sun, Z., & Kong, X.-Z. (2021) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2021.11.001 · Cited by 22
A study on relational factors in information technology outsourcing: analyzing judgments of small and medium-sized supplying and contracting companies' managers
de Carvalho, V. D. H., Poleto, T., Nepomuceno, T. C. C., & Costa, A. P. C. S. (2021) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-10-2020-0475 · Cited by 25
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“In 1965, Zadeh [1] introduced the term fuzzy sets (FSs), which is extensively used in different fields such as life sciences, social sciences, engineering, theory of decision making, computer sciences etc. Subsequently, many generalizations of the fuzzy sets have been explored in the literature like interval-valued fu…”
MethodInfluential
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“Based on linguistic term sets (Zadeh, 1975) and Z-numbers (Zadeh, 2011), the concept of linguistic Z-numbers was introduced by Wang et al. (2017) to express both vagueness and randomness of uncertain linguistic information.”
Background
Sustainability assessment using a fuzzy DEA aggregation approach: a healthcare application
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“According to Zadeh (1975; 1983), a linguistic variable such as age is one that its value is linguistic instead of numerical and it can also be used to tackle incomplete information.”
Background
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Ozceylan, E., Ozkan, B., Kabak, M., & Dağdeviren, M. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-219186 · Cited by 22
Retracted
Dynamic programming algorithm-based picture fuzzy clustering approach and its application to the large-scale group decision-making problem
Pan, X.-H., Wang, Y., & Chin, K. (2021) · Computers & industrial engineering · doi:10.1016/j.cie.2021.107330 · Cited by 22
Uncertainty-based rainfall network design using a fuzzy spatial interpolation method
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Homotopy analysis Shehu transform method for solving fuzzy differential equations of fractional and integer order derivatives
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“In the same year, L-relation Salii (1965) was presented, which is more general in a theoretical context and applies to decision-making (Kuzmin 1982; Khana et al. 2019a, b; Yen and Langari 1995), clustering (Bezdek 1978), and the linguistic (Zadeh 1975; Langari 1992).”
Background
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Liu, P.-D., Wang, X., & Teng, F. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-202543 · Cited by 22
“Based on score function D ( L 2 ( p ))anduncertainfunction F ( L 2 ( p )),thecom-parison rules of MGPLTSs are as follows: Deﬁnition 11 .”
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Liu, Y., Wei, G., Liu, H., & Xu, L. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01262-9 · Cited by 22
Urban transportation system long-term resilience assessment using multi-dimensional dynamic Bayesian network
Wang, N., Wu, M., Yuen, K. F., & Gao, X. (2024) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2024.104427 · Cited by 22
A novel interval type-2 fuzzy consensus reaching process model and group decision-making method for renewable energy investment
Wang, S., Pan, X., Martínez, L. M., & Albarracín, A. L. M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108422 · Cited by 22
Multiple criteria decision-making based on linguistic spherical fuzzy copula extended power aggregation operators
Ali, J. (2024) · Granular Computing · doi:10.1007/s41066-024-00464-8 · Cited by 21
“To express assessment data in the form of LVs, Zadeh (1975) introduced the concept of linguistic term sets (LTSs).”
Evaluating sustainability of urban mobility of Asian cities: An integrated approach of interval type-2 fuzzy best-worst method and MULTIMOORA
Yucesan, M., Özkan, B., Mete, S., Gul, M., & Özceylan, E. (2024) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2023.107266 · Cited by 21
Multi-attribute group decision-making based on 2-tuple linguistic cubic q-rung orthopair fuzzy DEMATEL analysis
Naz, S., Saeed, M. R., & Butt, S. A. (2023) · Granular Computing · doi:10.1007/s41066-023-00433-7 · Cited by 21
“The linguistic variable (LV), introduced by Zadeh (1975), serves as a method for describing qualitative information in DM scenarios.”
Enhancing System Safety and Reliability through Integrated FMEA and Game Theory: A Multi-Factor Approach
Yazdi, M. (2023) · Safety · doi:10.3390/safety10010004 · Cited by 21
“Zadeh [115] argues for the concept of linguistic variables and their practical applications.”
Background
Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods
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Khan, A., Ahmad, U., & Shahzadi, S. (2023) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-023-08263-0 · Cited by 21
Retracted
Applications of Fuzzy Theory-Based Approaches in Tunnelling Geomechanics: a State-of-the-Art Review
Madanda, V. C., Sengani, F., & Mulenga, F. (2023) · Mining Metallurgy & Exploration · doi:10.1007/s42461-023-00767-5 · Cited by 21
A Fully Completed Spherical Fuzzy Data-Driven Model for Analyzing Employee Satisfaction in Logistics Service Industry
Nguyen, P. (2023) · Mathematics · doi:10.3390/math11102235 · Cited by 21
Social network trust relationship environment based advanced ovarian cancer treatment decision-making model: An approach based on linguistic information with experts' multiple confidence levels
Mandal, P., Samanta, S., Pal, M., & Ranadive, A. (2023) · Expert systems with applications · doi:10.1016/j.eswa.2023.120407 · Cited by 21

Authors: Zadeh, L. A.
Affiliations: University of California, Berkeley (US)
Corresponding author: Zadeh, L.A.
Venue: Information Sciences
Publisher: Elsevier BV
Published: 1975
Volume: 8
Issue: 3
Pages: 199-249
ISSN: 0020-0255 (ISSN-L)
DOI: 10.1016/0020-0255(75)90036-5
OpenAlex: W4245152641
Cited by: 12,187
References: 48
Citations by year: 2026: 309 · 2025: 516 · 2024: 569 · 2023: 707 · 2022: 682 · 2021: 746 · 2020: 693 · 2019: 759 · 2018: 655 · 2017: 598 · 2016: 551 · 2015: 546 · 2014: 484 · 2013: 437 · 2012: 367
FWCI: 13.65
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Information Sciences (publishedVersion)
Funders: Army Research Office (DA-ARO-D-31-124-7 l-G1 74)
National Science Foundation (GK-VP-610656X3)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Algebra and Logic
Physical Sciences › Computer Science › Artificial Intelligence › Mathematical Control Systems and Analysis
Keywords: linguistic variables, approximate reasoning, fuzzy logic, compatibility function, hedges, linguistic truth values, nonlinear programming, human decision-making, fuzzy sets, artificial intelligence

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