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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Zadeh, 1973, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Syst., Man and Cybern, SMC-3, 28, 10.1109/TSMC.1973.5408575 · DOI
Bellman, 1966, Abstraction and pattern classification, J. Math. Anal. Appl., 13, 1, 10.1016/0022-247X(66)90071-0 · DOI
Black, 1963, Reasoning with loose concepts, Dialogue, 2, 1, 10.1017/S001221730004083X · DOI
Scriven, 1959, The logic of criteria, J. Philos., 56, 857, 10.2307/2022316 · DOI
Khatchadourian, 1965, Vagueness, meaning and absurdity, Am. Phil. Quart., 2, 119
Verma, 1970, Vagueness and the principle of excluded middle, Mind, 79, 66
Goguen, 1969, The Logic of Inexact Concepts, Synthese, 19, 325, 10.1007/BF00485654 · DOI
Adams, 1974, The logic of “Almost All”, J. Philos. Logic, 3, 3, 10.1007/BF00652068 · DOI
Zadeh, 1966, Shadows of fuzzy sets, Probl. Transfer Inf., 2, 37
Goguen, 1967, L-fuzzy sets, J. Math. Anal. Appl., 18, 145, 10.1016/0022-247X(67)90189-8 · DOI
Brown, 1971, A note on fuzzy sets, Inf. Control, 18, 32, 10.1016/S0019-9958(71)90288-9 · DOI
Mizumoto, 1972, General formulation of formal grammars, Inf. Sci., 4, 87, 10.1016/0020-0255(72)90006-0 · DOI
Zadeh, 1971, Similarity relations and fuzzy orderings, Inf. Sci., 3, 177, 10.1016/S0020-0255(71)80005-1 · DOI
Bellman, 1970, Decision-making in a fuzzy environment, Manage. Sci., 17, Bdash141, 10.1287/mnsc.17.4.B141 · DOI
Bellman, 1973, On the analytic formalism of the theory of fuzzy sets, Inf. Sci., 5, 149, 10.1016/0020-0255(73)90009-1 · DOI
Zadeh, 1972, A fuzzy-set-theoretic interpretation of linguistic hedges, J. Cybern., 2, 4, 10.1080/01969727208542910 · DOI
Codd, 1971, Relational completeness of data base sublanguages, Vol. 6
Zadeh, 1965, Fuzzy sets, Inf. Control, 8, 338, 10.1016/S0019-9958(65)90241-X · DOI · Paper page
Brzozowski, 1965, Regular expressions for linear sequential circuits, IEEE Trans. Electron Comput., EC-14, 148, 10.1109/PGEC.1965.263959 · DOI
Blikle, 1972, Equational languages, Inf. Control, 21, 134, 10.1016/S0019-9958(72)90063-0 · DOI
Rosenkrantz, 1967, Matrix equation and normal forms for context-free grammars, J. Assoc. Comput. Mach., 14, 501, 10.1145/321406.321412 · DOI
Zadeh, 1971, Quantitative fuzzy semantics, Inf. Sci., 3, 159, 10.1016/S0020-0255(71)80004-X · DOI
Knuth, 1968, Semantics of context-free languages, Math. Syst. Theory, 2, 127, 10.1007/BF01692511 · DOI
Wegner, 1972, The Vienna definition language, ACM Comput. Surv., 4, 5, 10.1145/356596.356598 · DOI
Barkan, 1946, A functional calculus of first order based on strict implication, J. Symbol. Logic, 11, 1, 10.2307/2269159 · DOI
Zadeh, 1968, Probability measures of fuzzy events, J. Math. Anal. Appl., 23, 421, 10.1016/0022-247X(68)90078-4 · DOI
DeLuca, 1972, A definition of non-probabilistic entropy in the setting of fuzzy set theory, Inf. Control, 20, 201, 10.1016/S0019-9958(72)90199-4 · DOI
DeLuca, 1972, Algebraic properties of fuzzy sets, J. Math. Anal. Appl., 40, 373, 10.1016/0022-247X(72)90057-1 · DOI
Rosenfeld, 1971, Fuzzy groups, J. Math. Anal. Appl., 35, 512, 10.1016/0022-247X(71)90199-5 · DOI
Zadeh, 1968, Fuzzy Algorithms, Inf. Control, 12, 94, 10.1016/S0019-9958(68)90211-8 · DOI
Santos, 1970, Fuzzy algorithms, Inf. Control, 17, 326, 10.1016/S0019-9958(70)80032-8 · DOI
Chang, 1972, On the execution of fuzzy programs using finite state machines, IEEE Trans. Electron. Comput., C-21, 241, 10.1109/TC.1972.5008946 · DOI
Chang, 1972, Fuzzy mapping and control, IEEE Trans. Syst., Man and Cybern., SMC-2, 30, 10.1109/TSMC.1972.5408553 · DOI
Lee, 1972, Fuzzy logic and the resolution principle, J. Assoc. Comput. Mach., 19, 109, 10.1145/321679.321688 · DOI
Colby, 1971, Artificial paranoia, J. Artif. Intell., 2, 1, 10.1016/0004-3702(71)90002-6 · DOI

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“There are several computational models developed to perform linguistic computations [2], [46] based on the fuzzy linguistic approach [4], [50], [51].”
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“Since its creation by Zadeh [59], [62], [63] fuzzy linguistic approach has occupied a prominent place in a wide variety of application domains in science, engineering, and business, and has been vigorously”
BackgroundMethod
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“Various researchers such as Zadeh (1965, 1968, 1973, 1975, 1978), Mamdani and Gains (1981), and Lee (1990) have extended the application of fuzzy systems and their various attributes.”
Background
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“First, fuzzy numbers can represent the values of linguistic variables (Zadeh, 1975a, 1975b, 1975c) when deciding either on the importance of criteria or performing qualitative evaluation of alternatives.”
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“, the name s , its semantics ( ) M s and its restriction ( ) R s can be used interchangeably [31].”
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“Similarly, it is also the case for the term very very young, here h ¼ very very and X p ¼ young: It was shown in [20] that we can construct a context-free grammar for generating the term-set of a Boolean linguistic variable.”
Background
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“the concept of fuzzy sets (Zedah 1975); the rough data are reasoned by the rough”
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“The first category is the study on interval-valued fuzzy set theory (IVF in short), which is original proposed by Zadeh in 1970s [43,44].”
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“Let a μ ν = ( , ) a a and b μ ν = ( , ) b b be any two q‐ ROFNs, then (1) ∨ a b μ μ ν ν = (max( , ), min( , )) a b a b ; (2) ∧ a b μ μ ν ν = (min( , ), max( , )) a b a b ; (3) ⊕ a b μ μ μ μ ν ν = ( ( ) + ( ) − ( ) ( ) , ) a q b q a q b q a b q ; (4) ⊗ a b μ μ ν ν ν ν = ( , ( ) + ( ) − ( ) ( ) ) a b a q b q a q b q q ;…”
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“The concept of a linguistic variable was first introduced by Zadeh in [23]–[25].”
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“Meanwhile, the combination of linguistic variables and economics assists to collect much natural linguistic information and reveal some undiscovered laws of economic motion (Zadeh, 1975).”
Background
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“Type-2 fuzzy sets [39], [40], [41] are an extension of type1 fuzzy sets in which the sets’ membership grades are themselves type-1 fuzzy sets.”
Background
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“In the latter case, the use of the fuzzy linguistic approaches [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] has provided very good results.”
Influential
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“A linguistic variable is a variable whose values are linguistic terms [26].”
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“Fuzzy time series Based on Zadeh’s methods (Zadeh, 1965, 1975a, 1975b, 1975c), Song and Chissom (1993) first proposed a forecasting model called Fuzzy Time Series, which provided a theoretic framework to model a special dynamic process whose observations are linguistic values.”
Method
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“(2012) brought up the hesitant fuzzy LTSs (HFLTSs) with the aid of HFSs (Torra 2010) and LTSs (Zadeh 1975) which allowed DMs to provide some possible LTSs.”
Method
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“IFS [41], PFS [42], type-2 fuzzy sets [43], hesitant fuzzy sets [44], and neutrosophic sets (NS) [45] are the most used sets in the literature [27].”
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“At this point, fuzzy set theory (Zadeh 1975), by utilizing fuzzy sets, is very suitable to transform linguistic evaluations of DMs to a numerical form and handle vagueness.”
Retracted
Linguistic majorities with difference in support
Pérez-Asurmendi, P., & Chiclana, F. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.01.010 · Cited by 54
“In these cases is still valid the following quotation by Zadeh [38]: “Since words, in general, are less precise than numbers, the concept of a linguistic variable serves the purpose of providing a means of approximate characterization of phenomena which are too complex or too ill-defined to be amenable to description…”
BackgroundInfluential
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Zhu, C.-X., Zhu, L., & Zhang, X.-Z. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.011 · Cited by 53
European option pricing under fuzzy environments
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Pacheco, A. D., Álvarez‐Carmona, M. Á., Rodríguez, R. G., Chávez, L. A. C., Rodríguez‐González, A. Y., Ramírez-Silva, J. P., & Aranda, R. (2022) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2022.2153276 · Cited by 21
“This theory was introduced by Zadeh in 1965 and uses fuzzy sets to try to reproduce human behaviour under certain conditions (L. Zadeh, 1975, L. Zadeh, 1975).”
The Generalized Dice Similarity Measures for Probabilistic Uncertain Linguistic MAGDM and Its Application to Location Planning of Electric Vehicle Charging Stations
Wei, G., Lin, R., Lu, J., Wu, J., & Wei, C. (2021) · International Journal of Fuzzy Systems · doi:10.1007/s40815-021-01084-z · Cited by 52
Optimization of fuzzy controller design using a Differential Evolution algorithm with dynamic parameter adaptation based on Type-1 and Interval Type-2 fuzzy systems
Ochoa, P., Castillo, O., & Soria, J. (2019) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-019-04156-3 · Cited by 53
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Linguistic terms with weakened hedges: A model for qualitative decision making under uncertainty
Wang, H., Xu, Z.-S., & Zeng, X.-J. (2018) · Information Sciences · doi:10.1016/j.ins.2017.12.036 · Cited by 51
“The powering hedge [63] would be the most widely acknowledged kind of hedges.”
BackgroundMethodInfluential
Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model
Cheng, C.-H., Chen, Y.-S., & Wu, Y.-L. (2009) · Expert systems with applications · doi:10.1016/j.eswa.2007.12.041 · Cited by 51
“Fuzzy theory was originally developed to deal with the problems involving human linguistic terms (Zadeh, 1975a; Zadeh, 1975b; Zadeh, 1976).”
Background
Fuzzy Qualitative Trigonometry
Liu, H.-H., Coghill, G., & Barnes, D. P. (2005) · IEEE International Conference on Systems, Man and Cybernetics · doi:10.1016/j.ijar.2009.07.003 · Cited by 51
Computer Models of Speech Using Fuzzy Algorithms
Mori, R. D. (1983) · Advanced Applications in Pattern Recognition · doi:10.1007/978-1-4613-3742-3 · Cited by 51
Sustainability evaluation of photovoltaic poverty alleviation projects using an integrated MCDM method: A case study in Guangxi, China
Wei, Q. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127040 · Cited by 50
“IT2F sets was put forward by Zadeh (1975) as the extension of fuzzy sets to deal with uncertain linguistic variables.”
Background
The need for fuzzy AI
Garibaldi, J. M. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911465 · Cited by 56
“In 1973 [10] and then his seminal papers of 1975 [11], [12], [13] he introduced the full framework of fuzzy logic, including linguistic variables, specifically to be used “in the realm of humanistic systems” which he defined to include AI and human decision processes.”
Background
Intuitionistic hesitant linguistic sets and their application in multi-criteria decision-making problems
Zhou, H. M., Wang, J., Li, X.-E., & Wang, J. (2015) · Operational Research · doi:10.1007/s12351-015-0199-4 · Cited by 49
“The fuzzy linguistic approach (Zadeh 1975a, b, c) was proposed during the 1970s and has since received popular recognition and wide investigation.”
Measuring consistency of linguistic preference relations: a 2-tuple linguistic approach
Dong, Y.-C., Hong, W.-C., & Xu, Y.-F. (2013) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-013-1031-9 · Cited by 50
“When a decision problem is solved using linguistic information, it implies the need for computing with words (CW) (Herrera et al. 2009; Martı´nez et al. 2010; Mendel 2009; Rodrı´guez et al. 2012; Zadeh 1975).”
A hybrid multi-stage predictive model for supply chain network collapse recovery analysis: a practical framework for effective supply chain network continuity management
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Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF
Vasant, P., Bhattacharya, A., Sarkar, B., & Mukherjee, S. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2006.10.005 · Cited by 50
“(8) was indicated as S-curve MF by Goguen [16] and Zadeh [46] [47] [48] [49].”
Method
Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method
Vahdani, B., & Zandieh, M. (2010) · doi:10.1080/00207540902933155 · Cited by 49
“3.1.1 Fuzzy numbers In this section, some basic definitions of fuzzy sets, fuzzy numbers are reviewed from Zadeh (1975a, 1975b), Buckley (1985), Negi (1989), Kaufmann and Gupta (1991).”
BackgroundInfluential
Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series
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Extension of the ARAS Method for Decision-Making Problems with Interval-Valued Triangular Fuzzy Numbers
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“Linguistic Variables In a series of papers, Zadeh (1975a, 1975b, 1975c) introduced the concept of linguistic variables.”
The Fuzzy Revolution: Goodbye to the Aristotelian Weltanschauung
Sadegh-Zadeh, K. (2001) · Artif. Intell. Medicine · doi:10.1016/s0933-3657(00)00071-3 · Cited by 47
COVID-19 crisis and resilience of tourism SME's: a focus on policy responses
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“…approach in dealing with real-world issues, several developments of fuzzy sets have been presented over the past few decades such as intuitive fuzzy sets (Atanassov, 1986), type 2 fuzzy sets (Zadeh, 1975), fuzzy multisets (Yager, 1986) and hesitant fuzzy sets (Torra, 2010; Torra & Narukawa, 2009).”
Implementation of Adjusted Fuzzy AHP Method in the Assessment for Reuse of Industrial Buildings
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Bridging uncertainty concepts across narratives and simulations in environmental scenarios
Pedde, S., Kok, K., Onigkeit, J., Brown, C., Holman, I., & Harrison, P. (2019) · Regional Environmental Change · doi:10.1007/s10113-018-1338-2 · Cited by 46
Risk Analysis Model with Interval Type-2 Fuzzy FMEA – Case Study of Railway Infrastructure Projects in the Republic of Serbia
Macura, D. V., Laketić, M., Pamucar, D. S., & Marinković, D. (2022) · Acta Polytechnica Hungarica · doi:10.12700/aph.19.3.2022.3.9 · Cited by 59
“Fuzzy Logic, introduced by Zadeh in 1965 [24, 25], is used when there is a lack of information or knowledge for presenting the values of real-world parameters.”
Linguistic description of the human gait quality
Alvarez-Alvarez, A., & Triviño, G. (2013) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2012.01.022 · Cited by 46
Deep feature extraction and classification of breast ultrasound images
Kriti, Virmani, J., & Agarwal, R. (2020) · Multimedia tools and applications · doi:10.1007/s11042-020-09337-z · Cited by 45
A fuzzy linguistic algorithm for adaptive test in Intelligent Tutoring System based on competences
Badaracco, M., & Martínez, L. (2013) · Expert systems with applications · doi:10.1016/j.eswa.2012.12.023 · Cited by 45
“…(L. j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a using experts' knowledge linguistically modeled (Herrera & Martínez , 2000; Zadeh, 1975a, 1975b, 1975c) to avoid calibration, and it includes a dynamic component in the selection process.”
Background
Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates
Karam, S., Nagahi, M., Dayarathna, V. L., Ma, J.-F., Jaradat, R. M., & Hamilton, M. A. (2020) · Expert systems with applications · doi:10.1016/j.eswa.2020.113585 · Cited by 43
A TOPSIS method by using generalized trapezoidal hesitant fuzzy numbers and application to a robot selection problem
Deli, I. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179448 · Cited by 43
Creating knowledge databases for storing and sharing people knowledge automatically using group decision making and fuzzy ontologies
Morente-Molinera, J. A., Pérez, I. J., Ureña, R., & Herrera-Viedma, E. (2016) · Information Sciences · doi:10.1016/j.ins.2015.08.051 · Cited by 43
Human activity recognition in indoor environments by means of fusing information extracted from intensity of WiFi signal and accelerations
Alvarez-Alvarez, A., Alonso, J., & Triviño, G. (2013) · Information Sciences · doi:10.1016/j.ins.2013.01.029 · Cited by 43
Qualitative Process Theory Using Linguistic Variables
D'Ambrosio, B. (1989) · Symbolic Computation · doi:10.1007/978-1-4613-9671-0 · Cited by 43
A study of plithogenic graphs: applications in spreading coronavirus disease (COVID-19) globally
Sultana, F., Gulistan, M., Ali, M., Yaqoob, N., Khan, M., Rashid, T., & Ahmed, T. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-03772-6 · Cited by 26
“Further, Zadeh (1975) extended fuzzy sets (FS) to interval- valued fuzzy sets in which values of membership are intervals instead of real numbers between 0 and 1.”
Background
Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods
Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Dehghan-Sanej, K., & Kaboli, A. (2021) · doi:10.1108/ecam-08-2020-0617 · Cited by 42
Determining the Critical Factors of Radical Innovation Using an Integrated Model of Fuzzy Analytic Hierarchy Process-Fuzzy Kano With a Case Study in Mobarakeh Steel Company
Shahin, A., Barati, A., & Geramian, A. (2017) · Engineering Management Journal · doi:10.1080/10429247.2017.1298182 · Cited by 39
“Fuzzy sets were introduced by Zadeh (1965, 1975a, 1975b, 1975c) to consider uncertainty and ambiguity hidden in human judgments.”
Method
Some two-dimensional uncertain linguistic Heronian mean operators and their application in multiple-attribute decision making
Chu, Y.-C., & Liu, P.-D. (2015) · Neural computing & applications (Print) · doi:10.1007/s00521-014-1813-8 · Cited by 42
Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting
Khashei, M., & Bijari, M. (2014) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-130775 · Cited by 42
Automatically Generated Linguistic Summaries of Energy Consumption Data
Heide, A. J. V. D., & Triviño, G. (2009) · 2009 Ninth International Conference on Intelligent Systems Design and Applications · doi:10.1109/isda.2009.8 · Cited by 42
“As the concepts ‘morning’, ‘midday’, ‘afternoon’, ‘evening’, and ‘night’ are not clearly bounded in time, fuzzy logic can be an excellent tool to model this [14], [15], [16].”
Using the Hamming distance to extend TOPSIS in a fuzzy environment
Izadikhah, M. (2009) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2009.02.102 · Cited by 42
“The concept of linguistic variables is useful in dealing with situations that are too complex or too poorly defined to be reasonably described in conventional quantitative expressions [17].”
Logic-oriented fuzzy neural networks: A survey
Alateeq, M., & Witold Pedrycz, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125120 · Cited by 49
Analysis of occupational health hazards and associated risks in fuzzy environment: a case research in an Indian underground coal mine
Samantra, C., Datta, S., & Mahapatra, S. (2017) · International Journal of Injury Control and Safety Promotion · doi:10.1080/17457300.2016.1178298 · Cited by 40
Fuzzy time series forecasting of nonstationary wind and wave data
Stefanakos, C. (2016) · Ocean Engineering · doi:10.1016/j.oceaneng.2016.05.018 · Cited by 40
Intuitionistic Fuzzy Logic Control for Heater Fans
Akram, M., Shahzad, S., Butt, A., & Khaliq, A. (2013) · Mathematics and Computer Science · doi:10.1007/s11786-013-0161-x · Cited by 40
RETRACTED: Probabilistic linguistic GRA method for multiple attribute group decision making
Wei, G., Lu, J., Wei, C., & Wu, J. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-191416 · Cited by 39
Retracted
Model for selecting a route for the transport of hazardous materials using a fuzzy logic system
Milošević, T., Pamucar, D., & Chatterjee, P. (2021) · Vojnotehnički Glasnik · doi:10.5937/vojtehg69-29629 · Cited by 39
“The average response time is taken as the input parameter.”
Background
Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches
Bairagi, B., Dey, B., Sarkar, B., & Sanyal, S. (2014) · doi:10.1080/17509653.2014.880076 · Cited by 38
Complete fuzzy scheduling and fuzzy earned value management in construction projects
Ponz-Tienda, J., Pellicer, E., & Yepes, V. (2012) · doi:10.1631/jzus.a1100160 · Cited by 38
“(8) The methodology for deciding the values for limits is critical to the TFS. Basically, there are two kinds of computing techniques: Zadeh (1975)’s method based on the extension principles and Lawry (2001)’s method based on the mass assignment theory.”
An application of fuzzy Topsis method for supplier selection
Sevkli, M., Zaim, S., Turkyilmaz, A., & Satir, M. (2010) · International Conference on Fuzzy Systems · doi:10.1109/fuzzy.2010.5584006 · Cited by 38
“The main applications of the linguistic approach lie in the realm of humanistic systems especially in the fields of artificial intelligence, linguistics, human decision processes, pattern recognition, psychology, law, medical diagnosis, information retrieval, economics and related areas [28].”
Background
Properties of the First Type of Covering-Based Rough Sets
Zhu, W., & Wang, F.-Y. (2006) · Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) · doi:10.1109/icdmw.2006.136 · Cited by 38
“In order to cope with this issue, researchers have developed many techniques such as fuzzy set theory [40], rough set theory [14], computing with words [21, 36, 37, 38, 39], computational theory for linguistic dynamic systems [22], and granular computing [1, 9, 10, 35, 34].”
Method
Combinative distance-based assessment method using linguistic T-spherical fuzzy aggregation operators and its application to multi-attribute group decision-making
Gurmani, S. H., Zhang, S., Awwad, F. A., & Ismail, E. A. A. (2024) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2024.108165 · Cited by 37
A two-stage decision framework for GIS-based site selection of wind-photovoltaic-hybrid energy storage project using LSGDM method
Gao, J.-W., Wang, Y.-P., Guo, F., & Chen, J. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.119912 · Cited by 37
Multi-criteria group decision-making based on spherical fuzzy rough numbers
Akram, M., & Ashraf, M. (2023) · Granular Computing · doi:10.1007/s41066-023-00412-y · Cited by 37
“Researchers (Zadeh 1975a,b,c), Szmidt and Kacprzyk (2000), Dubois and Prade (1978), Chen and Chen (2001), Chen et al. (2009), Chen and Wang (2010), Chen and Niou (2011), and Chen and Jian (2017) have explored the versatile applications of fuzzy sets.”
Background
Grey Earned Value Management: Theory and Applications
Mahmoudi, A., Bagherpour, M., & Javed, S. (2021) · IEEE transactions on engineering management · doi:10.1109/tem.2019.2920904 · Cited by 37
“Fuzzy rationale indicates uncertainty and more information allows us to become more precise about the degree of membership [61], [62].”
Background
Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller
Peraza, C., Valdez, F., Castro, J. R., & Castillo, O. (2018) · Advances in Operations Research · doi:10.1155/2018/3092872 · Cited by 37
A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images
Guo, Y.-H., Budak, Ü., Şengür, A., & Smarandache, F. (2017) · Symmetry · doi:10.3390/sym9100235 · Cited by 37

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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