Cited by 2,555Multi-Criteria Decision MakingFuzzy and Soft Set TheoryRough Sets and Fuzzy Logic

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

Zadeh, L.A.·1975·Information Sciences

DOI 10.1016/0020-0255(75)90046-8

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Items 201–300 of 890

Extension of GRA method for multiattribute group decision making problem under linguistic Pythagorean fuzzy setting with incomplete weight information
Khan, M. S. A., Jana, C., Khan, M. T., Mahmood, W., Pal, M., & Mashwani, W. K. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23003 · Cited by 36
“By using the distance measure presented in Equation (17), Equations (18) and (19) determine the positive ideal separation matrix and negative ideal separation matrix respectively, indicated as follows: and Step 6.”
MethodInfluential
Decision-making from multiple uncertain experts: case of distribution center location selection
Agrebi, M., & Abed, M. (2020) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-020-05461-y · Cited by 36
“…(MAADM) method (Agrebi et al. 2017) based on ELECTRE Imethod (Milosavljević et al. 2018; Collette and Siarry 2011; Roy 1968) and the multiple criteria decision-making method (Chen 2001) based on the fuzzy set theory (Si et al. 2019; Yager 1996; Zadeh 1975a, b, c; Zadeh et al. 1965).”
RETRACTED: MABAC method for multiple attribute group decision making with probabilistic uncertain linguistic information
Wei, G., He, Y., Lei, F., Wu, J., & Wei, C. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-191688 · Cited by 36
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Linguistic Z‐number fuzzy soft sets and its application on multiple attribute group decision making problems
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A Fuzzy Trade-Off Ranking Method for Multi-Criteria Decision-Making
Jaini, N., & Utyuzhnikov, S. (2017) · Axioms · doi:10.3390/axioms7010001 · Cited by 36
Usability evaluation of website using combined weighted method: fuzzy AHP and entropy approach
Nagpal, R., Mehrotra, D., & Bhatia, P. (2016) · International Journal of System Assurance Engineering and Management · doi:10.1007/s13198-016-0462-y · Cited by 36
Intuitionistic Fuzzy Logic Control for Washing Machines
Akram, M., Habib, S., & Javed, I. (2014) · doi:10.17485/ijst/2014/v7i5/50070 · Cited by 36
Some types of generalized fuzzy filters of BL-algebras
Zhan, J., & Xu, Y. (2008) · Computers and Mathematics with Applications · doi:10.1016/j.camwa.2008.03.009 · Cited by 36
“[18,19]) an interval [a, a], where 0 ≤ a ≤ a ≤ 1.”
The Central Limit Theorems for Fuzzy Random Variables
Wu, H.-C. (1999) · Information Sciences · doi:10.1016/s0020-0255(99)00063-8 · Cited by 36
Selection of Unmanned Aerial Vehicles for Precision Agriculture Using Interval-Valued q-Rung Orthopair Fuzzy Information based TOPSIS Method
Gurmani, S. H., Garg, H., Zulqarnain, R. M., & Siddique, I. (2023) · International Journal of Fuzzy Systems · doi:10.1007/s40815-023-01568-0 · Cited by 35
“In this study, linguistic terms induced by linguistic variables by Zadeh [65] are used to describe the grade of each option and the importance of the attributes.”
Method
Large-scale group consensus hybrid strategies with three-dimensional clustering optimisation based on normal cloud models
Liu, W., Zhu, J., & Chiclana, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.017 · Cited by 35
“The second one is based on the mapping of the finite set of linguistic preferences into a finite set of fuzzy sets [65].”
Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags
Gupta, P., Govindan, K., Mehlawat, M., & Khaitan, A. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1888392 · Cited by 35
“Zadeh (1975a, 1975b, 1975c) defined linguistic variables, fuzzy rules, and relations, with membership values representing the strength of the relation.”
Background
Multiple attribute group decision making based on 2-dimension linguistic intuitionistic fuzzy aggregation operators
Verma, R., & Merigó, J. (2020) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-020-05026-z · Cited by 35
Simple Additive Weighting Method Equipped with Fuzzy Ranking of Evaluated Alternatives
Piasecki, K., Roszkowska, E., & Lyczkowska-Hanckowiak, A. (2019) · Symmetry · doi:10.3390/sym11040482 · Cited by 35
Fusion of probabilistic linguistic term sets for enhanced group decision-making: Foundations, survey and challenges
Ma, X., Han, X., Xu, Z., Rodríguez, R. M., & Zhan, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102802 · Cited by 34
Uncertain database retrieval with measure-based belief function attribute values
Yager, R., Alajlan, N., & Bazi, Y. (2019) · Information Sciences · doi:10.1016/j.ins.2019.03.074 · Cited by 34
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del Caño, A., de la Cruz, M. D. P., Gómez, D., & Pérez, M. (2016) · doi:10.3846/13923730.2014.928361 · Cited by 34
“Following on, a first possibility is to perform fuzzy operations with different input variables, applying the Zadeh Extension Principle (Zadeh 1975a, 1975b, 1975c).”
Fuzzy Logic for Social Simulation Using NetLogo
Izquierdo, L. R., Olaru, D., Izquierdo, S. S., Purchase, S., & Soutar, G. (2015) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.2885 · Cited by 34
“Early on, the approach taken by Zadeh to formalise vagueness started to be applied to "fuzzify" many disciplines by virtue of Zadeh's "extension principle" (Zadeh 1975a, 1975b, 1975c).”
BackgroundMethodInfluential
An extension of the MOORA method for solving fuzzy decision making problems
Stanujkić, D. (2014) · doi:10.3846/20294913.2013.880083 · Cited by 34
QUALIFLEX method for MAGDM with probabilistic uncertain linguistic information and its application to green supplier selection
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Assessing the impact of construction industry stakeholders on workers' unsafe behaviours using extended decision making approach
Khoshnava, S. M., Rostami, R., Zin, R. M., Mishra, A., Rani, P., Mardani, A., & Alrasheedi, M. A. (2020) · doi:10.1016/j.autcon.2020.103162 · Cited by 33
Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation
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“The use of linguistic expressions [31] in pairwise comparisons allows the decision makers to make a more realistic and natural assessments.”
Type-2 Fuzzy Envelope of Hesitant Fuzzy Linguistic Term Set: A New Representation Model of Comparative Linguistic Expression
Liu, Y.-Y., Rodríguez, R. M., Hagras, H., Liu, H.-B., Qin, K., & Martínez, L. (2019) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2019.2898155 · Cited by 33
“A T2 FS, initiated by Zadeh [4], [51], [52] as an extension of an ordinary fuzzy set (type-1 fuzzy set), is denoted by Ã and characterized by a type-2 membership function μÃ (x, u) [22], [25], where x ∈ X and”
Background
Fuzzy time series model based on weighted association rule for financial market forecasting
Cheng, C.-H., & Chen, C.-H. (2018) · Expert Syst. J. Knowl. Eng. · doi:10.1111/exsy.12271 · Cited by 33
Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral
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“A linguistic variable [26], [27] is defined by a triple value (V , X, Tv), where V , X, and Tv are defined as follows.”
Background
pg4DB: A fuzzy object-relational system
Cuevas, L., Marín, N., Pons, O., & Vila, M. (2008) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2008.01.009 · Cited by 33
A General Framework for Computing with Words in Object-Oriented Programming
Berzal, F., Cubero, J., Marín, N., Vila, M., Kacprzyk, J., & Zadrożny, S. (2007) · Int. J. Uncertain. Fuzziness Knowl. Based Syst. · doi:10.1142/s0218488507004480 · Cited by 33
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Chen, S.-M. (2000) · Cybernetics and systems · doi:10.1080/019697200750038977 · Cited by 33
“Suppose a linguistic variable (Zadeh, 1975a,b,c) V is de¢ned, which contains linguistic terms v1; v2; . . . ; vn.”
Background
Pricing European Option Under Fuzzy Mixed Fractional Brownian Motion Model with Jumps
Zhang, W., Li, Z., Liu, Y.-J., & Zhang, Y. (2020) · Computational Economics · doi:10.1007/s10614-020-10043-z · Cited by 32
“Proposition 1 (Resolution identity, Zadeh 1975a, b, c) Let Ã be a fuzzy set with membership function 𝜇Ã and –level set Ã𝛼 = { x ∶ 𝜇Ã(x) ≥ 𝛼} .”
Soft failure mode and effects analysis using the OWG operator and hesitant fuzzy linguistic term sets
Chang, K.-H., Wen, T.-C., & Chung, H.-Y. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-17594 · Cited by 32
Multiattribute decision method for comprehensive logistics distribution center location selection based on 2-dimensional linguistic information
Liu, P.-D., & Li, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.131 · Cited by 31
A new computational model based on Archimedean copula for probabilistic unbalanced linguistic term set and its application to multiple attribute group decision making
Han, B., Tao, Z., Chen, H., Zhou, L.-G., & Liu, J.-P. (2020) · Computers & industrial engineering · doi:10.1016/j.cie.2019.106264 · Cited by 31
Bidirectional Projection Method for Probabilistic Linguistic Multi-criteria Group Decision-Making Based on Power Average Operator
Liu, P.-D., Li, Y., & Teng, F. (2019) · International Journal of Fuzzy Systems · doi:10.1007/s40815-019-00705-y · Cited by 31
Computing with Words: Revisiting the Qualitative Scale
Zuheros, C., Li, C.-C., Cabrerizo, F., Dong, Y.-C., Herrera-Viedma, E., & Herrera, F. (2018) · Int. J. Uncertain. Fuzziness Knowl. Based Syst. · doi:10.1142/s0218488518400147 · Cited by 31
The PROMTHEE II Method Based on Probabilistic Linguistic Information and Their Application to Decision Making
Liu, P.-D., & Li, Y. (2018) · Informatica · doi:10.3233/inf-2018-1177 · Cited by 31
Multiple attribute group decision-making methods based on trapezoidal fuzzy two-dimension linguistic power generalized aggregation operators
Li, Y., Wang, Y.-M., & Liu, P.-D. (2015) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-015-1668-7 · Cited by 31
“…of people’s thinking judgment and complexity of objective thing, which is more reasonable and natural to utilize linguistic information such as good, fair, poor or fuzzy concept such as slightly, obviously, mightily (Zadeh 1975a,b,c; Delgado et al. 1993; Xu 2002, 2008; Xu and Da 2003; Chu 2002).”
Background
Granular Box Regression
Peters, G. (2011) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2011.2162416 · Cited by 31
Solving Fully Fuzzy Linear System with the Necessary and Sufficient Condition to have a Positive Solution
Malkawi, G., Ahmad, N., & Ibrahim, H. (2014) · doi:10.12785/amis/080309 · Cited by 30
The Effects of Three Facets of National Identity and Other Socioeconomic Traits on Attitudes Towards Immigrants
Indelicato, A., & Martín, J. C. (2023) · Journal of International Migration and Integration · doi:10.1007/s12134-023-01100-1 · Cited by 29
“FST is used effectively to cope with vagueness in decision-making (Dubois, 1980; Kaya, 2014; Klir & Yuan, 1995; Ross, 2005; Zadeh, 1975; Zimmermann, 2011).”
New form of single valued neutrosophic uncertain linguistic variables aggregation operators for decision-making
Fan, C., Fan, E., & Hu, K. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.10.004 · Cited by 29
Continuous-state reliability measures based on fuzzy sets
Li, Z., & Kapur, K. (2012) · doi:10.1080/0740817x.2011.588684 · Cited by 29
A gamma type-2 defuzzification method for solving a solid transportation problem considering carbon emission
Sengupta, D., Das, A., & Bera, U. (2018) · Applied intelligence (Boston) · doi:10.1007/s10489-018-1173-7 · Cited by 28
“Since the invention, of the type-1 fuzzy set (T1FS) by Zadeh [17] in 1965, the fuzzy sets are hugely used in many areas of artificial intelligence.”
BackgroundInfluential
A knowledge-based outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets
Sellak, H., Ouhbi, B., & Frikh, B. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.06.031 · Cited by 28
“Page 4 of 27 Ac ce pt ed M an us cr ip t Zadeh [9] first introduced the linguistic variables in the 1970s as an extension of classical fuzzy 105 models.”
BackgroundInfluential
Use of the Triangular Fuzzy Numbers for Student Assessment
Voskoglou, M. (2015) · arXiv.org · doi:10.12691/ajams-3-4-2 · Cited by 28
“Fuzzy logic, the development of which is based on fuzzy sets theory [16], provides a rich and meaningful addition to standard Boolean logic.”
Background
Basic Concepts in Covering-Based Rough Sets
Zhu, W. (2007) · International Conference on Computing, Networking and Communications · doi:10.1109/icnc.2007.281 · Cited by 28
“To this end, researchers have proposed many methods other than classical logic, for example, fuzzy set theory [22], rough set theory [8], computing with words [15, 23, 24, 25, 26], granular computing [5, 21, 19], computational theory for linguistic dynamic systems [16].”
Method
A new model for linguistic modifiers
Dilascio, L., Gisolfi, A., & Loia, V. (1996) · International Journal of Approximate Reasoning · doi:10.1016/0888-613x(95)00133-2 · Cited by 28
“222] = E[~] = An tonym E[9] = An tonym(Very Very Very True) = Very Very Very False [by considering relation (d)].”
Background
Experiments on content based image classification using Color feature extraction
Shinde, S., Sabale, S., Kulkarni, S., & Bhatia, D. (2015) · International Conference on Communication, Information & Computing Technology · doi:10.1109/iccict.2015.7045737 · Cited by 28
Dynamic collaborative optimization for disaster relief supply chains under information ambiguity
Zhu, J.-X., Shi, Y.-Y., Venkatesh, V., Islam, S., Hou, Z.-P., & Arisian, S. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04758-5 · Cited by 27
“Deﬁnition 1 If A α denotes an interval type-1 fuzzy trapezoidal set, α denotes the degree of , 1975), then ˜ ˜ A denotes an extension of the IT1TFS A μ , as shown in Eq.”
Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain
Monje, L., Carrasco, R. A., Rosado, C., & Sánchez-Montañés, M. A. (2022) · Mathematics · doi:10.3390/math10091428 · Cited by 36
“To increase the interpretability of these variables, we propose the use of fuzzy linguistic variables which were originally proposed by Zadeh [8].”
Integrated BWM-Entropy weighting and MULTIMOORA method with probabilistic linguistic information for the evaluation of Waste Recycling Apps
Ma, Y., Zhao, Y., Wang, X.-Y., Feng, C., Zhou, X.-Y., & Lev, B. (2022) · Applied intelligence (Boston) · doi:10.1007/s10489-022-03377-8 · Cited by 27
“To closely present the expert’s cognition and perceptions, Zadeh [46] put forward the linguistic terms to express their judgments.”
Background
Linguistic interval-valued q-rung orthopair fuzzy TOPSIS method for decision making problem with incomplete weight
Khan, M. S. A., Khan, A. S., Khan, I., Mashwani, W. K., & Hussain, F. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-200845 · Cited by 27
“To overcome this limitation, the idea of linguistic term set (LTS) was ﬁrst introduced by Zadeh [36, 37].”
The reference ideal TOPSIS method for linguistic q-rung orthopair fuzzy decision making based on linguistic scale function
Liu, D., Liu, Y., & Wang, L. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-200244 · Cited by 27
On Application of Ordered Fuzzy Numbers in Ranking Linguistically Evaluated Negotiation Offers
Piasecki, K., & Roszkowska, E. (2018) · Advances in Fuzzy Systems · doi:10.1155/2018/1569860 · Cited by 27
Uncertainty in Interval Type-2 Fuzzy Systems
Aminifar, S., & Marzuki, A. (2013) · doi:10.1155/2013/452780 · Cited by 27
An integrated CRITIC and EDAS model using linguistic T spherical fuzzy Hamacher aggregation operators and its application to group decision making
Rasool, Z., Gurmani, S. H., Niazai, S., Zulqarnain, R. M., Alballa, T., & Khalifa, H. (2025) · Scientific Reports · doi:10.1038/s41598-024-81825-w · Cited by 26
Graph model for conflict resolution based on the combination of probabilistic uncertain linguistic and EDAS method
Liu, P., Wang, X., Fu, Y., & Wang, P. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120116 · Cited by 26
Group decision-making method based on advanced aggregation operators with entropy and divergence measures under 2-tuple linguistic Pythagorean fuzzy environment
Verma, R., & Álvarez‐Miranda, E. (2023) · Expert systems with applications · doi:10.1016/j.eswa.2023.120584 · Cited by 26
A knowledge reduction approach for linguistic concept formal context
Zou, L., Pang, K., Song, X., Kang, N., & Liu, X. (2020) · Information Sciences · doi:10.1016/j.ins.2020.03.002 · Cited by 26
The generalized fuzzy derivative is interactive
Wasques, V., Esmi, E., Barros, L., & Sussner, P. (2020) · Information Sciences · doi:10.1016/j.ins.2020.01.042 · Cited by 26
ANFIS and Deep Learning based missing sensor data prediction in IoT
Güzel, M., Kök, İ., Akay, D., & Özdemi̇r, S. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5400 · Cited by 31
“Therefore, we applied grid search method under different number of layers (1, 2, 3), hidden units (50, 120, 240, 480) and batch size (60, 72, 240, 480, 1440) for preliminary model optimization.”
Method
Intuitionistic Type-2 Fuzzy Set and Its Properties
Dan, S., Kar, M. B., Majumder, S., Roy, B., Kar, S., & Pamucar, D. (2019) · Symmetry · doi:10.3390/sym11060808 · Cited by 26
A New Intuitionistic Fuzzy Linguistic Hybrid Aggregation Operator and Its Application for Linguistic Group Decision Making
Xian, S.-D., Jing, N., Xue, W., & Chai, J. (2017) · International Journal of Intelligent Systems · doi:10.1002/int.21902 · Cited by 26
SPARQL queries on RDF with fuzzy constraints and preferences
Ma, R.-Z., Jia, X.-Y., Cheng, J., & Angryk, R. (2015) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-151745 · Cited by 26
Comparison of Hybrid Intelligent Systems, Neural Networks and Interval Type-2 Fuzzy Logic for Time Series Prediction
Castillo, O., & Melin, P. (2007) · 2007 International Joint Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371453 · Cited by 26
“Uncertainty is an attribute of information [20].”
Novel similarity measure between hesitant fuzzy set and their applications in pattern recognition and clustering analysis
Gupta, R., & Kumar, S. (2024) · Journal of engineering and applied sciences · doi:10.1186/s44147-023-00329-y · Cited by 25
RETRACTED: Some self-evaluation models of enterprise’s credit based on some probabilistic double hierarchy linguistic aggregation operators
Lei, F., Wei, G., & Chen, X.-D. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-202922 · Cited by 25
Retracted
Generalized similarity measures under linguistic q-rung orthopair fuzzy environment with application to multiple attribute decision-making
Verma, R. (2021) · Granular Computing · doi:10.1007/s41066-021-00264-4 · Cited by 25
Modeling agent‐based consumers decision‐making with 2‐tuple fuzzy linguistic perceptions
Giráldez-Cru, J., Chica, M., Cordón, O., & Herrera, F. (2019) · International Journal of Intelligent Systems · doi:10.1002/int.22211 · Cited by 25
“We model the latter brand perceptions by means of fuzzy linguistic variables.(14-16) This 284 | GIRÁLDEZ‐CRU ET AL.”
Method
The likelihood interpretation as the foundation of fuzzy set theory
Cattaneo, M. (2017) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2017.08.006 · Cited by 25
Improving data exploration in graphs with fuzzy logic and large-scale visualisation
Molina-Solana, M., Birch, D., & Guo, Y.-K. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2016.12.044 · Cited by 25
“Fuzzy logic precisely enables this type of intuitive reasoning, and thus closes the gap between human reasoning and mathematical reasoning [25].”
Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems
López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2009) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2009.02.013 · Cited by 25
“So, some fuzzy linguistic IRS models [10] [11] [12] [13] [14] [15] [16] [17] [18] have been proposed using a fuzzy linguistic approach [19] [20] [21] to model the query weights.”
Fuzzy inference system for software product family process evaluation
Ahmed, F., Capretz, L. F., & Samarabandu, J. (2008) · Information Sciences · doi:10.1016/j.ins.2008.03.002 · Cited by 25
“There are two major reasons for using the TS-fuzzy model in comparison to the Zadeh [31] [32] [33] [34] or Mamdani [19] models in our proposed approach.”
Method
Multicriteria Security System Performance Assessment Using Fuzzy Logic
McGill, W. L., & Ayyub, B. (2007) · doi:10.1177/154851290700400405 · Cited by 25
“A linguistic variable is one that takes on linguistic values such as “strong” for a variable describing impact resistance of a crash barrier and “secure” for a variable defining the state of protection [11].”
An integrated SWOT-based interval type-2 fuzzy AHP and TOPSIS methodology for digital transformation strategy selection in maritime safety
Gulen, M. F., Uflaz, E., Gumus, F., Orhan, M., & Arslan, O. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.120518 · Cited by 24
An Entropy-Based Method for Probabilistic Linguistic Group Decision Making and its Application of Selecting Car Sharing Platforms
Xu, G.-L., Wan, S., & Dong, J.-Y. (2020) · Informatica · doi:10.15388/20-infor423 · Cited by 24
“DMs are more apt to use linguistic variables (Zadeh, 1975) to evaluate alternatives.”
A Multi-Attribute Group Decision-Making Method Based on Linguistic Intuitionistic Fuzzy Numbers and Dempster-Shafer Evidence Theory
Liu, P.-D., Liu, X., Ma, G., Liang, Z., Wang, C., & Alsaadi, F. (2020) · International Journal of Information Technology and Decision Making · doi:10.1142/s0219622020500042 · Cited by 24
An ordinal multi-criteria decision-making procedure under imprecise linguistic assessments
García-Lapresta, J., & del Pozo, R. D. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.05.015 · Cited by 24
“[7]), the vast majority of methods manage linguistic terms through cardinal approaches or fuzzy techniques (see Zadeh [28], Herrera-Viedma and López-Herrera [15] and Herrera et al.”
Background
Hesitant Fuzzy Linguistic Aggregation Operators Based on the Hamacher t-norm and t-conorm
Zhu, J., & Li, Y. (2018) · Symmetry · doi:10.3390/sym10060189 · Cited by 24
“The Fuzzy linguistic method (FLM) [2,12,13], which decision makers prefer to provide an evaluation for using a linguistic term, is a more suitable approach than the above fuzzy set to handle qualitative situations and has been extensively applied in various ﬁelds and applications [14–18].”
Method
Multisourcing suppliers selection in service outsourcing
Feng, B. (2012) · Journal of the Operational Research Society · doi:10.1057/jors.2011.61 · Cited by 24
“In this section, we introduce some deﬁnitions of interval number, fuzzy number and linguistic variable (Zadeh, 1965, 1975; Delgado et al 1998, Jiang et al , 2008).”
Method
Analysis of Renewable Energies Based on Circular Bipolar Complex Intuitionistic Fuzzy Linguistic Information with Frank Power Aggregation Operators and MABAC Model
Ali, Z., & Yang, M.-S. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00800-z · Cited by 23
A Linguistic Metric for Consensus Reaching Processes Based on ELICIT Comprehensive Minimum Cost Consensus Models
García-Zamora, D., Labella, Á., Rodríguez, R. M., & Martínez, L. (2023) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2022.3213943 · Cited by 23
“Tooffermorerealisticandsuitableframeworks forDMstoexpresstheirpreferencesaccordingtotheirnatural wayofthinking,theuseofthefuzzylinguisticapproachand linguisticvariables[4],[5],[6]hasincreaseditspopularityin recentyears.”
BackgroundMethodInfluential
Fuzzy hypothesis testing: Systematic review and bibliography
Chukhrova, N., & Johannssen, A. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107331 · Cited by 23
Linguistic Pythagorean Einstein Operators and Their Application to Decision Making
Yuan, R., Zheng, P., & Yi, L. (2020) · Inf. · doi:10.3390/info11010046 · Cited by 23
The New Similarity Measure and Distance Measure of a Hesitant Fuzzy Linguistic Term Set Based on a Linguistic Scale Function
Liu, D., Liu, Y., & Chen, X. (2018) · Symmetry · doi:10.3390/sym10090367 · Cited by 23
A hybrid approach for building a Vietnamese sentiment dictionary
Tran, T. K., & Phan, T. T. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-172053 · Cited by 25
“According to Zadeh’s work [28], if the adjective had a preceding adverb, its modiﬁed fuzzy score was computed by where δ can be 2 or 1/2.”
Pairwise Comparison and Distance Measure of Hesitant Fuzzy Linguistic Term Sets
Huang, H.-C., & Yang, X.-J. (2014) · doi:10.1155/2014/954040 · Cited by 23
Probability representations of fuzzy systems
Li, H.-X. (2006) · Science in China Series F · doi:10.1007/s11432-006-0339-9 · Cited by 23
A multi-criteria group decision-making method based on fuzzy rough number for optimal water supply strategy
Akram, Z., & Ahmad, U. (2023) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-023-08942-y · Cited by 22
Retracted
Generalized Cubic Pythagorean Fuzzy Aggregation Operators and their Application to Multi-attribute Decision-Making Problems
Amin, F., Rahim, M., Ali, A., & Ameer, E. (2022) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-022-00145-x · Cited by 22
Homotopy analysis Shehu transform method for solving fuzzy differential equations of fractional and integer order derivatives
Maitama, S., & Zhao, W.-D. (2021) · Computational and Applied Mathematics · doi:10.1007/s40314-021-01476-9 · Cited by 22
Subjective stakeholder dynamics relationships treatment: a methodological approach using fuzzy decision-making
Blanco-Mesa, F., Gil-Lafuente, A., & Merigó, J. (2018) · Computational and mathematical organization theory · doi:10.1007/s10588-018-09284-z · Cited by 22
“The studies on fuzzy decision-making stemmed from studies of the concepts of fuzzy sets (Zadeh 1965), fuzzy environments (Bellman and Zadeh 1970), approximate reasoning (Zadeh 1975a, b, c) and applications of fuzzy sets in decision systems (Zimmermann 1986).”
BackgroundMethod
On the quality of resources on the Web: An information retrieval perspective
van Gils, B., Proper, H., van Bommel, P., & van der Weide, T. P. (2007) · Information Sciences · doi:10.1016/j.ins.2007.05.035 · Cited by 22
“The example membership function given in [Zad75b] for the variable young is as follows: Even more, if the interpretation of the hedge very is the square of the term to which it belongs then the interpretation of very old is the square of the above function.”
MethodInfluential
Properties of the Third Type of Covering-Based Rough Sets
Zhu, W., & Wang, F.-Y. (2007) · International Conference on Machine Learning and Computing · doi:10.1109/icmlc.2007.4370799 · Cited by 22
“In order to cope with this issue, researchers have developed many techniques such as fuzzy set theory [56], rough set theory [22], computing with words [38, 57, 58, 59, 60], formal concept analysis [40, 41], quotient space theory [63, 64], computational theory for linguistic dynamic systems [39], and granular computin…”
Method
A fuzzy semantic representation and reasoning model for multiple associative predicates in knowledge graph
Li, P., Wang, X., Liang, H., Zhang, S.-Z., Zhang, Y., Jiang, Y.-C., & Tang, Y. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.079 · Cited by 21
A Novel Decision-Making Method Based on Probabilistic Linguistic Information
Liu, P.-D., & Li, Y. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09648-w · Cited by 21
A four-way decision-making approach using interval-valued fuzzy sets, rough set and granular computing: a new approach in data classification and decision-making
Singh, P., & Huang, Y.-P. (2019) · Granular Computing · doi:10.1007/s41066-019-00165-7 · Cited by 21
“In literature, among several types of fuzzy sets, interval-valued fuzzy set (IVFS) presented by Zadeh (1975) has been observed to be appropriate for managing uncertainty.”
Background
THE IMPORTANCE OF ENTERPRISE RISK MANAGEMENT IN LARGE COMPANIES IN COLOMBIA
Blanco-Mesa, F., Rivera-Rubiano, J., Patiño-Hernandez, X., & Martinez-Montaña, M. (2019) · Technological and Economic Development of Economy · doi:10.3846/tede.2019.9380 · Cited by 21
“…studies have been carried out based on fuzzy sets concept (Zadeh, 1965), fuzzy environments (Bellman & Zadeh, 1970), the approximate reasoning (Zadeh, 1975a, 1975b, 1975c), aggregation operators (Yager, 1988), averaging functions (Beliakov, Bustince Sola, & Calvo Sánchez, 2016) and so on.”
Method
A Review of Healthcare Failure Mode and Effects Analysis (HFMEA) in Radiotherapy
Giardina, M., Cantone, M., Tomarchio, E., & Veronese, I. (2016) · Health Physics · doi:10.1097/hp.0000000000000536 · Cited by 21
A Social Choice Analysis of the Borda Rule in a General Linguistic Framework
García-Lapresta, J., Llamazares, B., & Martínez-Panero, M. (2010) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2010.9727717 · Cited by 21
Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX
Liu, J.-W., Chen, T.-L., Cheng, C.-H., & Chen, Y.-H. (2010) · Computers and Mathematics with Applications · doi:10.1016/j.camwa.2009.10.014 · 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: 1975-01
Volume: 8
Issue: 4
Pages: 301-357
ISSN: 0020-0255 (ISSN-L)
DOI: 10.1016/0020-0255(75)90046-8
OpenAlex: W4240278385
Cited by: 2,555
References: 0
Citations by year: 2026: 42 · 2025: 72 · 2024: 83 · 2023: 106 · 2022: 135 · 2021: 130 · 2020: 134 · 2019: 133 · 2018: 122 · 2017: 100 · 2016: 115 · 2015: 136 · 2014: 95 · 2013: 83 · 2012: 69
FWCI: 13.96
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Information Sciences (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
Social Sciences › Decision Sciences › Management Science and Operations Research › Fuzzy and Soft Set Theory
Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
Keywords: ELECTRE II, multi-criteria group decision-making, outranking relations, fuzzy ELECTRE, linguistic variables, approximate reasoning

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

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