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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A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method
Jaber, H., Marle, F., Vidal, L.-A., Sarigol, I., & Didiez, L. (2021) · Sustainability · doi:10.3390/su13063020 · Cited by 13
“The linguistic variable might be adopted to provide approximate characterization when conventional quantitative terms are complex or ill-defined [93].”
Method
Dual generalized Bonferroni mean operators based on 2-dimensional uncertain linguistic information and their applications in multi-attribute decision making
Liu, P.-D., & Liu, W. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09857-y · Cited by 13
Selecting technological alternatives for regulatory compliance towards emissions reduction from shipping: An integrated fuzzy multi-criteria decision-making approach under vague environment
Bui, K. Q., Ölçer, A., Kitada, M., & Ballini, F. (2020) · Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment · doi:10.1177/1475090220917815 · Cited by 13
“The fuzzy TOPSIS evaluates the criteria weights and alternatives ratings by fuzzy linguistics evaluation based on fuzzy set theory.(10,11) There are several benefits of the TOPSIS and fuzzy TOPSIS technique.”
Method
Developing a computationally effective Interval Type-2 TSK Fuzzy Logic Controller
Hailemichael, A., Salaken, S. M., Karimoddini, A., Homaifar, A., Abbas, K., & Nahavandi, S. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190446 · Cited by 13
“Interval type-2 FLSs [2, 23, 25–28] reduce the computational complexity of the general type-2 FLSs, while preserving the major advantages of general type-2 FLSs, particularly, the ability of handling uncertainty [29–31].”
Uncertain multi-objective multi-item fixed charge solid transportation problem with budget constraint
Majumder, S., Kundu, P., Kar, S., & Pal, T. (2018) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-017-2987-7 · Cited by 13
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Mehlawat, M., & Grover, N. (2017) · Annals of Operations Research · doi:10.1007/s10479-017-2477-4 · Cited by 13
“The fuzzy set theory proposed by Zadeh (1965, 1975) plays a significant role in these situations to handle the vagueness/ambiguity in a project network (Mon et al. 1995; Slyeptsov and Tyshchuk 2003; Zielinski 2005; Shankar et al. 2010).”
Background
The convolution algebra
Harding, J., Walker, C., & Walker, E. (2017) · doi:10.1007/s00012-018-0510-3 · Cited by 13
“While the convolution algebra has a predecessor in Zadeh’s algebra of type-2 fuzzy sets [10, 20, 21], the general definition of the convolution algebra of a relational structure over a complete lattice seems new to this paper.”
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Uncertainty Measurement for the Interval Type-2 Fuzzy Set
Greenfield, S. (2016) · International Conference on Artificial Intelligence and Soft Computing · doi:10.1007/978-3-319-39378-0_17 · Cited by 13
Fuzzy Bayesian system reliability assessment based on Pascal distribution
Gholizadeh, R., Shirazi, A. M., Gildeh, B. S., & Deiri, E. (2009) · doi:10.1007/s00158-009-0396-y · Cited by 13
“Proposition 2.1 ( Zadeh 1975a b, c; Resolution Identity ) Letã be a fuzzy subset of with membership function ξ ˜ a .”
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Multi-criteria decision making with Hamacher aggregation operators based on multi-polar fuzzy Z-numbers
Ullah, I., Akram, M., & Allahviranloo, T. (2025) · Information Sciences · doi:10.1016/j.ins.2024.121707 · Cited by 12
Possibilistic C-means with novel image representation for image segmentation
Cui, H.-S., Wang, H., Zeng, W., Liu, Y., & Zhao, B. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11057-x · Cited by 12
“Therefore, various types of fuzzy sets have been introduced for different scenarios, including the interval-valued fuzzy set (Zadeh 1975a, b , c ), intuitionistic fuzzy set (Atanassov 1986, 1989), fuzzy multisets (Miyamoto 2000; Yager 1986), and hesitant fuzzy set (Torra and Narukawa 2009).”
A Binary Risk Linguistic Fuzzy Behavioral TOPSIS Model for Multi-attribute Large-Scale Group Decision-Making Based on Risk Preference Classification and Adaptive Weight Updating
Huang, A., Yang, Y., & Liu, Y. (2024) · International Journal of Fuzzy Systems · doi:10.1007/s40815-024-01710-6 · Cited by 12
A Fuzzy Inference Model to Identify the Current Industry Maturity Stage in the Transformation Process to Industry 4.0
Gomes, A. D. O., & Basilio, J. C. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3242225 · Cited by 12
“Truth is actually a linguistic variable whose truth-values are taken from fuzzy numbers representing terms such as true, not true, completely true, essentially true, fairly true, more or less true, etc. [20], [21].”
Background
Optimizing Inventory Management: A Comprehensive Analysis of Models Integrating Diverse Fuzzy Demand Functions
Mittal, M., Jain, V., Pandey, J., Jain, M., & Dem, H. (2023) · Mathematics · doi:10.3390/math12010070 · Cited by 12
“Type 2 fuzzy sets are a sophisticated extension of type 1 fuzzy sets, originally introduced by Zadeh [7,64].”
Background
An Integrative Decision-Making Mechanism for Consumers’ Brand Selection using 2-Tuple Fuzzy Linguistic Perceptions and Decision Heuristics
Giráldez-Cru, J., Chica, M., & Cordón, O. (2022) · International Journal of Fuzzy Systems · doi:10.1007/s40815-022-01385-x · Cited by 12
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Guo, K., Li, X.-F., Zang, H.-R., & Fan, T.-H. (2020) · Entropy · doi:10.3390/e22121423 · Cited by 12
“Intuitionistic fuzzy set is an improvement of the traditional fuzzy set [23–25].”
Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure
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Verma, R. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181197 · Cited by 12
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Nagarajan, D., Kavikumar, J., Lathamaheswari, M., & Kumaresan, N. (2019) · International Journal of Fuzzy Systems · doi:10.1007/s40815-019-00738-3 · Cited by 12
“The aggregation operators are based on alpha-cut decomposition theorem [26, 27].”
Method
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Zavadskas, E., Kaklauskas, A., Kalibatas, D., Turskis, Z., Krutinis, M., & Bartkiene, L. (2018) · doi:10.30638/eemj.2018.203 · Cited by 12
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“After that, it has been successfully used in different areas [2-7].”
Method
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Liu, S., Triantis, K., Zhao, L., & Wang, Y.-F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194687 · Cited by 12
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“The fuzzy set theory was initiated by Zadeh (1965, 1975).”
Method
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Subramanian, C., Krishna, A., Kaur, A., & Gopalan, R. P. (2015) · International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2015-158 · Cited by 12
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Background
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Ketsbaia, L., Issac, B., Chen, X.-M., & Jacob, S. M. (2023) · IEEE Access · doi:10.1109/access.2023.3282834 · Cited by 12
“Fuzzy Logic takes values between 0 and 1 and uses expressions written in IF-THEN rules [57].”
Method
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Saqlain, M., Merigó, J. M., Kumam, P., & Riaz, M. (2025) · Punjab University Journal of Mathematics · doi:10.52280/pujm.2025.57(03)01 · Cited by 9
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“According to the principles of fuzzy set theory [36], the optimization of multiple workﬂow applications scheduling in Authorized licensed use limited to the terms of the applicable license agreement with IEEE.”
Method
Hybrid computer aided diagnostic system designs for screen film mammograms using DL ‐based feature extraction and ML ‐based classifiers
Rani, J., Singh, J., & Virmani, J. (2023) · Expert Systems · doi:10.1111/exsy.13309 · Cited by 13
“Thereby neural (Cetisli, 2010a, 2010b; Manogaran et al., 2018; Zadeh et al., 1975a, 1975b, 1975c).”
Extended PROMETHEE approach with 2‐tuple linguistic m‐polar fuzzy sets for selection of elliptical cardio machine
Akram, M., Noreen, U., & Pamucar, D. (2022) · Expert Syst. J. Knowl. Eng. · doi:10.1111/exsy.13178 · Cited by 11
“Zadeh (1975a, 1975b, 1975c, 1983) presented remarkable contributions to the linguistic representation of data in decision analysis.”
Background
Robot Selection Using An Integrated MAGDM Model Based on ELECTRE Method and Linguistic q-Rung Orthopair Fuzzy Information
Bao, H., & Shi, X. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/1444486 · Cited by 11
Generalized extension principle for non-normal fuzzy sets
Wu, H.-C. (2019) · Fuzzy Optimization and Decision Making · doi:10.1007/s10700-019-09307-7 · Cited by 11
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Ngan, S.-C. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2018.03.061 · Cited by 11
Heronian Mean Operator of Linguistic Neutrosophic Cubic Numbers and Their Multiple Attribute Decision-Making Methods
Fan, C., & Ye, J. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/4158264 · Cited by 11
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Simões-Marques, M., & Figueira, J. (2018) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-94334-3_14 · Cited by 11
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Pan, W.-E., Wang, F.-X., Guo, Y., & Liu, S. (2015) · doi:10.1155/2015/174585 · Cited by 11
“[22] L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, pp. 338–353, 1965.”
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Social network group decision-making method based on stochastic multi-criteria acceptability analysis for probabilistic linguistic term sets
Xu, Z., Xu, H., Li, P., & Wei, C. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121269 · Cited by 10
Dynamic nonlinear simplified neutrosophic sets for multiple-attribute group decision making
Qiu, J.-D., Jiang, L., Fan, H.-H., Li, P., & You, C.-Z. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27493 · Cited by 10
The dual interactions-driven consensus model: Probing the non-cooperative behavior through order effects
Tan, X., Zhu, J., Wei, G., & Tong, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102212 · Cited by 10
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Naz, S., Shafiq, A., & Abbas, M. (2023) · Journal of Supercomputing · doi:10.1007/s11227-023-05678-2 · Cited by 10
Multi-attribute decision-making model based on regret theory and its application in selecting human resource service companies in the post-epidemic era
Liu, P., Pan, Q., Zhu, B.-Y., & Wu, X.-M. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119676 · Cited by 10
The Extended Linguistic Hellwig’s Methods Based on Oriented Fuzzy Numbers and Their Application to the Evaluation of Negotiation Offers
Roszkowska, E., Wachowicz, T., Filipowicz-Chomko, M., & Łyczkowska-Hanćkowiak, A. (2022) · Entropy · doi:10.3390/e24111617 · Cited by 10
“The linguistic variable is a variable whose values are not numbers but words or sentences expressed in a natural or artiﬁcial language [42–44].”
Background
Probabilistic linguistic decision-making based on the hybrid entropy and cross-entropy measures
Fang, B. (2022) · Fuzzy Optimization and Decision Making · doi:10.1007/s10700-022-09398-9 · Cited by 10
“To express the decision-maker’s qualitative evaluations, Zadeh has introduced the concept of linguistic variables in 1975 (Zadeh, 1975).”
Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes
Eghbali‐Zarch, M., Tavakkoli-Moghaddam, R. R., Esfahanian, F., & Masoud, S. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02602-3 · Cited by 11
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Mohamed, B., Haytam, H., & Fennan, A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-90087-8_19 · Cited by 13
Large-scale consensus with endo-confidence under probabilistic linguistic circumstance and its application
Li, W., Li, L., Xu, Z.-S., & Tian, X. (2021) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2021.1932546 · Cited by 10
“The linguistic variable (Zadeh, 1975) is closer to the natural or artificial language and has been widely used in the decision-making field.”
A novel method based on probabilistic linguistic term sets and its application in ranking products through online ratings
Fei, T., Liu, P.-D., & Witold, P. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22473 · Cited by 10
“t m ij ij tj tj C =1 =1 =1 j j j ∑ ∑∑ (7)”
A review on fuzzy preference modeling methods for group decision-making
Wang, G., Wu, L., Liu, Y.-S., & Ye, X. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-201529 · Cited by 10
“The three denotations, i.e., the name s , its semantics M ( s ) and its restriction R ( s ) can be used interchangeably [70].”
Combined probabilistic linguistic term set and ELECTRE II method for solving a venture capital project evaluation problem
Shen, F., Liang, C., & Yang, Z. (2021) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2021.1880957 · Cited by 10
A logical reasoning based decision making method for handling qualitative knowledge
Chen, S.-W., Liu, J., & Xu, Y. (2021) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2020.11.003 · Cited by 10
Automatically identifying performance issue reports with heuristic linguistic patterns
Zhao, Y., Xiao, L., Babvey, P., Sun, L., Wong, S., Martinez, A. A. G., & Wang, X. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409674 · Cited by 6
Maclaurin symmetric Means for Linguistic Z-numbers and Their Application to Multiple-Attribute Decision Making
Liu, P.-D., & Liu, W. (2020) · Scientia Iranica. International Journal of Science and Technology · doi:10.24200/sci.2020.52779.2880 · Cited by 10
Interval-valued fuzzy reasoning algorithms based on Schweizer-Sklar t-norms and its application
Luo, M.-X., Zhao, R., Liu, B., & Liang, J. (2020) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2019.103313 · Cited by 10
Impact of the Orientation of the Ordered Fuzzy Assessment on the Simple Additive Weighted Method
Piasecki, K., Roszkowska, E., & Lyczkowska-Hanckowiak, A. (2019) · Symmetry · doi:10.3390/sym11091104 · Cited by 10
Relation “Greater than or Equal to” between Ordered Fuzzy Numbers
Piasecki, K. (2019) · Applied System Innovation · doi:10.3390/asi2030026 · Cited by 10
The Fruit Fly Optimization Algorithms for Patient-Centered Care Based on Interval Trapezoidal Type-2 Fuzzy Numbers
Hu, J., Chen, P., Yang, Y., Liu, Y.-M., & Chen, X. (2019) · International Journal of Fuzzy Systems · doi:10.1007/s40815-019-00624-y · Cited by 10
Applying Fuzzy Multiple Criteria Decision Making approach to establish safety-management system for hot spring hotels
Fu, H.-H., & Tzeng, S.-Y. (2016) · doi:10.1080/10941665.2016.1175487 · Cited by 10
Accurate Semantic Similarity Measurement of Biomedical Nomenclature by Means of Fuzzy Logic
Martinez-Gil, J. (2016) · Int. J. Uncertain. Fuzziness Knowl. Based Syst. · doi:10.1142/s0218488516500148 · Cited by 10
Developing an Interval Type-2 TSK Fuzzy Logic Controller
Enyinna, N., Karimoddini, A., Opoku, D., Homaifar, A., & Arnold, S. (2015) · Annual Conference on the North American Fuzzy Information Processing Society · doi:10.1109/nafips-wconsc.2015.7284160 · Cited by 10
“The Type-2 fuzzy logic system (T-2 FLS), initially developed by Zahdeh [12], is unique in its ability to model and handle uncertainty while also being able to handle complex control structures and linguistic variables.”
Background
An AHP-PROMETHEE II method for 2-tuple linguistic multicriteria group decision making
Singh, A., Gupta, A., & Mehra, A. (2015) · 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) · doi:10.1109/icrito.2015.7359374 · Cited by 10
“The linguistic variables which participate in these problems are represented by means of linguistic terms [12], [13], [14], [15].”
Background
A new recursive type-reduction procedure for general type-2 fuzzy sets
Gafa, C., & Coupland, S. (2011) · IEEE Symposium on Type-2 Fuzzy Logic Systems · doi:10.1109/t2fuzz.2011.5949548 · Cited by 10
“Karnik and Mendel [1] used Zadeh’s extension principle [9], [10] to define the centroid of a general type2 fuzzy set, arriving at the same result as the representation theorem gives.”
Method
An Efficient Association Rule Mining Algorithm for Classification
Zemirline, A., Lecornu, L., Solaiman, B., & Ech-Cherif, A. (2006) · International Conference on Artificial Intelligence and Soft Computing · doi:10.1007/978-3-540-69731-2_69 · Cited by 10
“U is the universe of discourse and The terms of T ( x ) are characterized by fuzzy subsets deﬁned by the following membership functions [14]: –”
BackgroundMethodInfluential
Evaluating the Informative Quality of Web Sites by Fuzzy Computing with Words
Herrera‐Viedma, E., Peis, E., Olvera, M. D., Herrera, J. C., & Montero, Y. H. (2007) · Springer eBooks · doi:10.1007/3-540-44831-4_8 · Cited by 9
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García, R. E., Benjamín, G., & Pérez, R. B. (2014) · doi:10.4067/s0718-33052014000100005 · Cited by 10
“Formalmente una variable lingüística se define según [38-41] por <N, U, T(N), M> donde:”
Background
Row Reduced Echelon Form for Solving Fully Fuzzy System with Unknown Coefficients
Malkawi, G., Ahmad, N., Ibrahim, H., & Alshmari, B. (2014) · doi:10.5899/2014/jfsva-00193 · Cited by 10
Fuzzy-Evolutionary Modeling for Single-Position Day Trading
Pereira, C. D. C., & Tettamanzi, A. (2008) · Natural Computing in Computational Finance · doi:10.1007/978-3-540-77477-8_8 · Cited by 10
“A linguistic variable [36] is defined on a numerical interval and has linguistic values, whose semantics is defined by their membership function.”
Background
Decision-making using COPRAS method with linguistic q-rung orthopair fuzzy sets and Hamy mean operators
Khan, A., & Ali, J. (2025) · Journal of Applied Mathematics and Computation · doi:10.1007/s12190-025-02612-y · Cited by 9
“Zadeh pioneered the foundational concept of the linguistic term set (LTS) [ 4, 5 ] in 1975, providing a framework for expressing qualitative assessments—such as an expert describing a motorbike’s performance as “good” or “excellent.””
A Novel Multi-Criteria Decision-Making Framework of Vehicle Structural Factor Evaluation for Public Transportation Safety
Hou, Z., Lee, K.-M., Keung, K.-L., & Huang, J. (2025) · Applied Sciences · doi:10.3390/app15063045 · Cited by 9
Analysis of coupling in geographic information systems based on WASPAS method for bipolar complex fuzzy linguistic Aczel-Alsina power aggregation operators
Ali, Z., Hayat, K., & Pamucar, D. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309900 · Cited by 9
Self-Confidence Rationality Identification of Group Decision Making Based on Consensus Analysis
Meng, F., Lv, Y., & Wang, Z.-S. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3223795 · Cited by 9
“Qualitative variables introduced by Zadeh [53], [54], [55] are good measures, such as “unimportant,” “fair,” and “important.””
A novel cloud model based on multiplicative unbalanced linguistic term set
Chauhan, P., Gupta, A., & Malhotra, T. (2023) · Journal of Supercomputing · doi:10.1007/s11227-023-05295-z · Cited by 9
Development of a Robust Interval Type-2 TSK Fuzzy Logic Controlled UAV Platform
Hailemichael, A., & Karimoddini, A. (2023) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-022-01798-8 · Cited by 9
“Fuzzy logic controllers are exceptionally powerful in mimicking human decision-making by using membership functions and rule based inference mechanisms [33, 41, 48].”
Background
Correlation Coefficient and Entropy Measures Based on Complex Dual Type-2 Hesitant Fuzzy Sets and Their Applications
Mahmood, T., Ali, Z., Garg, H., Zedam, L., & Chinram, R. (2021) · Journal of mathematics · doi:10.1155/2021/2568391 · Cited by 9
“To survive such circumstances, the idea of type-2 FSs (T-2FSs) was presented by Zadeh [5], as a distinction from common FSs.”
Background
Retracted
RETRACTED: Research on bilateral matching decision method considering attribute association in heterogeneous information environment
Wang, R., Li, D., & Yu, G.-F. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-191495 · Cited by 11
“[38].”
Retracted
Certainty Factor Triunity in Medical Diagnostics Tasks
Kobrinskii, B. (2019) · Scientific and Technical Information Processing · doi:10.3103/s0147688219050046 · Cited by 9
The new similarity measure and distance measure between hesitant fuzzy linguistic term sets and their application in multi-criteria decision making
Liu, D., Liu, Y., & Chen, X. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181886 · Cited by 9
A systematic fuzzy multi-criteria group decision-making approach for alternatives evaluation
Peng, J.-G., & Xia, G. (2019) · Journal of the Operational Research Society · doi:10.1080/01605682.2018.1495995 · Cited by 9
“The FLA, as developed by Zadeh (1975a, 1975b, 1975c), interprets the term set and its semantics.”
Method
Some trapezoidal interval type-2 fuzzy Heronian mean operators and their application in multiple attribute group decision making
Wang, H.-H., Ju, Y., Liu, P.-D., Ju, D.-W., & Liu, Z.-M. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-17636 · Cited by 9
“Type-2 fuzzy sets (T2FSs), which were originally proposed by Zadeh [1, 2], have the capacity to depict higher order uncertainty associated with the natu-ral linguistic expressed by human beings during the course of multiple-attribute group decision-making (MAGDM).”
Outsourcing transport service: a fuzzy multi-criteria methodology for provider selection based on comparison of the real and ideal parameters of providers
Ljubojević, S., Pamucar, D., Jovanović, D., & Vesovic, V. (2017) · Operational Research · doi:10.1007/s12351-017-0293-x · Cited by 9
“To aggregate different experts’ opinions the fuzzy linguistic approach is used (Zadeh 1975; Rodrı́guez and Martı́nez 2013; Zhou et al. 2016).”
Computing sets of graded attribute implications with witnessed non-redundancy
Vychodil, V. (2015) · Information Sciences · doi:10.1016/j.ins.2016.03.004 · Cited by 9
On fuzzy logic and cognitive computing: Some perspectives
Gupta, M. (2011) · Scientia Iranica. International Journal of Science and Technology · doi:10.1016/j.scient.2011.04.010 · Cited by 9
Time Series Forecasting Using Hybrid Neuro-Fuzzy Regression Model
Chaudhuri, A., & De, K. (2009) · Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing · doi:10.1007/978-3-642-10646-0_45 · Cited by 9
A fuzzy group decision-making model with risk-taking attitudes in quality function deployment
Liu, C.-H. (2009) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-2009-0429 · Cited by 9
AN EVALUATION MODEL OF INNOVATION PERFORMANCE BASED ON FUZZY INTERVAL LINGUISTIC VARIABLES
Chen, C.-T., & Chen, P. (2009) · doi:10.1080/10170660909509153 · Cited by 9
“Zadeh [23-25] consider that it is difficult to define complex or indefinite issues by traditional quantification model.”
Background
Transitive closure of interval-valued relations
Gonzalez-del-Campo, R., Garmendia, L., & López, V. (2008) · 2008 3rd International Conference on Intelligent System and Knowledge Engineering · doi:10.1109/iske.2008.4731150 · Cited by 9
“One of these generalizations are type-2 fuzzy sets, FS 2 , [5, 6, 7] were introduced by Zadeh.”
Background
Equivalence class in the set of fuzzy numbers and its application in decision-making problems
Panda, G., Panigrahi, M., & Nanda, S. (2006) · International Journal of Mathematics and Mathematical Sciences · doi:10.1155/ijmms/2006/74165 · Cited by 9
“The arithmetic operations of fuzzy numbers have been performed either by extension principle [7, 8] or by using alpha cuts as discussed by Dubois and Prade [2].”
BackgroundMethod
Development of applications with fuzzy objects in modern programming platforms
Berzal, F., Marín, N., Pons, O., & Vila, M. (2005) · International Journal of Intelligent Systems · doi:10.1002/int.20114 · Cited by 9
Fuzzy defect avoidance system (FDAS) for product defect control
Stewart, D., Cheraghi, S., & Malzahn, D. (2004) · doi:10.1080/00207540410001696032 · Cited by 9
A Tribute to Father of Fuzzy Set Theory and Fuzzy Logic (Dr. Lotfi A.Zadeh)
Ashfaq, M. (2018) · doi:10.4172/2090-4908.1000170 · Cited by 9
Systemunterstützt individualisierte Kundenansprache in der Mehrkanalwelt der Finanzdienstleistungsbranche - Repräsentation der Einstellungen von Kunden in einem Kundenmodell
Buhl, H., Fridgen, M., & Volkert, S. (2003) · Wirtschaftsinformatik · doi:10.1007/978-3-642-57445-0_11 · Cited by 9
A Fuzzy AHP and PCA Approach to the Role of Media in Improving Education and the Labor Market in the 21st Century
Sančanin, B., Penjišević, A., Simjanović, D. J., Ranđelović, B. M., Vesić, N., & Mladenović, M. (2024) · Mathematics · doi:10.3390/math12223616 · Cited by 8
Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papers
Li, Y.-X., Kou, G., Peng, Y., & Morente-Molinera, J. A. (2024) · Applied intelligence (Boston) · doi:10.1007/s10489-024-05765-8 · Cited by 8
Fermatean fuzzy Linguistic term set based on linguistic scale function with Dombi aggregation operator and their application to multi criteria group decision –making problem
Barukab, O., Khan, A., & Khan, S. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36563 · Cited by 8
Solving Fuzzy Nonlinear Optimization Problems Using Null Set Concept
Sama, J. D. L. C., & Some, K. (2023) · International Journal of Fuzzy Systems · doi:10.1007/s40815-023-01626-7 · Cited by 8
Ratio-Type Estimator for Estimating the Neutrosophic Population Mean in Simple Random Sampling under Intuitionistic Fuzzy Cost Function
Ullah, A., Shabbir, J., Alomair, A., & Alomair, M. (2023) · Axioms · doi:10.3390/axioms12090890 · Cited by 8
“[13–15] developed the fuzzy sets theory to deal with decision-making problems under fuzzy uncertainty.”
Background

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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