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Journal articles on the topic 'Fuzzy logic'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

BOBILLO, FERNANDO, MIGUEL DELGADO, JUAN GÓMEZ-ROMERO, and UMBERTO STRACCIA. "JOINING GÖDEL AND ZADEH FUZZY LOGICS IN FUZZY DESCRIPTION LOGICS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 04 (2012): 475–508. http://dx.doi.org/10.1142/s0218488512500249.

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Ontologies have succeeded as a knowledge representation formalism in many domains of application. Nevertheless, they are not suitable to represent vague or imprecise information. To overcome this limitation, several extensions to classical ontologies based on fuzzy logic have been proposed. Even though different fuzzy logics lead to fuzzy ontologies with very different logical properties, the combined use of different fuzzy logics has received little attention to date. This paper proposes a fuzzy extension of the Description Logic [Formula: see text] — the logic behind the ontology language OW

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2

Metcalfe, George, and Franco Montagna. "Substructural fuzzy logics." Journal of Symbolic Logic 72, no. 3 (2007): 834–64. http://dx.doi.org/10.2178/jsl/1191333844.

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AbstractSubstructural fuzzy logics are substructural logics that are complete with respect to algebras whose lattice reduct is the real unit interval [0, 1]. In this paper, we introduce Uninorm logic UL as Multiplicative additive intuitionistic linear logic MAILL extended with the prelinearity axiom ((A → B) ∧ t) V ((B → A)∧ t). Axiomatic extensions of UL include known fuzzy logics such as Monoidal t-norm logic MIX and Gödel logic G, and new weakening-free logics. Algebraic semantics for these logics are provided by subvarieties of (representable) pointed bounded commutative residuated lattice

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3

Gehrke, Mai, Carol Walker, and Elbert Walker. "A Mathematical Setting for Fuzzy Logics." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 03 (1997): 223–38. http://dx.doi.org/10.1142/s021848859700021x.

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The setup of a mathematical propositional logic is given in algebraic terms, describing exactly when two choices of truth value algebras give the same logic. The propositional logic obtained when the algebra of truth values is the real numbers in the unit interval equipped with minimum, maximum and -x=1-x for conjunction, disjunction and negation, respectively, is the standard propositional fuzzy logic. This is shown to be the same as three-valued logic. The propositional logic obtained when the algebra of truth values is the set {(a, b)|a≤ b and a,b∈[0,1]} of subintervals of the unit interval

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4

MIYAKOSHI, Masaaki. "Fuzzy Logic." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 90–97. http://dx.doi.org/10.3156/jfuzzy.4.1_90.

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5

Chast, Roz. "Fuzzy Logic." Scientific American 289, no. 2 (2003): 96. http://dx.doi.org/10.1038/scientificamerican0803-96.

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Chast, Roz. "Fuzzy Logic." Scientific American 288, no. 6 (2003): 92. http://dx.doi.org/10.1038/scientificamerican0603-92.

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7

Klir, G. J. "Fuzzy logic." IEEE Potentials 14, no. 4 (1995): 10–15. http://dx.doi.org/10.1109/45.468220.

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Chast, Roz. "Fuzzy Logic." Scientific American 288, no. 3 (2003): 112. http://dx.doi.org/10.1038/scientificamerican0303-112.

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Chast, Roz. "Fuzzy Logic." Scientific American 287, no. 6 (2002): 139. http://dx.doi.org/10.1038/scientificamerican1202-139.

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Chast, Roz. "Fuzzy Logic." Scientific American 289, no. 6 (2003): 128. http://dx.doi.org/10.1038/scientificamerican1203-128.

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Chast, Roz. "Fuzzy Logic." Scientific American 286, no. 5 (2002): 104. http://dx.doi.org/10.1038/scientificamerican0502-104.

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Kosko, Bart, and Satoru Isaka. "Fuzzy Logic." Scientific American 269, no. 1 (1993): 76–81. http://dx.doi.org/10.1038/scientificamerican0793-76.

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Chast, Roz. "Fuzzy Logic." Scientific American 287, no. 2 (2002): 96. http://dx.doi.org/10.1038/scientificamerican0802-96.

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Chast, Roz. "Fuzzy Logic." Scientific American 290, no. 2 (2004): 100. http://dx.doi.org/10.1038/scientificamerican0204-100.

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Chast, Roz. "Fuzzy Logic." Scientific American 287, no. 3 (2002): 104. http://dx.doi.org/10.1038/scientificamerican0902-104.

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Chast, Roz. "Fuzzy Logic." Scientific American 289, no. 3 (2003): 116. http://dx.doi.org/10.1038/scientificamerican0903-116.

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Chast, Roz. "Fuzzy Logic." Scientific American 287, no. 5 (2002): 100. http://dx.doi.org/10.1038/scientificamerican1102-100.

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18

Lootsma, Freerk A. "Fuzzy logic." European Journal of Operational Research 102, no. 1 (1997): 244. http://dx.doi.org/10.1016/s0377-2217(97)90065-5.

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19

Sprawls, Clay. "Fuzzy logic." Journal of Strategic Information Systems 3, no. 1 (1994): 77. http://dx.doi.org/10.1016/0963-8687(94)90007-8.

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20

Novak, Kristine. "Fuzzy logic." Nature Reviews Cancer 2, no. 10 (2002): 724. http://dx.doi.org/10.1038/nrc919.

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21

Zadeh, Lotfi. "Fuzzy logic." Scholarpedia 3, no. 3 (2008): 1766. http://dx.doi.org/10.4249/scholarpedia.1766.

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22

Chast, Roz. "Fuzzy Logic." Scientific American 290, no. 1 (2004): 110. http://dx.doi.org/10.1038/scientificamerican0104-110.

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23

Zadeh, L. A. "Fuzzy logic." Computer 21, no. 4 (1988): 83–93. http://dx.doi.org/10.1109/2.53.

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Chast, Roz. "Fuzzy Logic." Scientific American 288, no. 2 (2003): 94. http://dx.doi.org/10.1038/scientificamerican0203-94.

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Chast, Roz. "Fuzzy Logic." Scientific American 287, no. 4 (2002): 108. http://dx.doi.org/10.1038/scientificamerican1002-108.

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Chast, Roz. "Fuzzy Logic." Scientific American 289, no. 4 (2003): 104. http://dx.doi.org/10.1038/scientificamerican1003-104.

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Chast, Roz. "Fuzzy Logic." Scientific American 286, no. 4 (2002): 100. http://dx.doi.org/10.1038/scientificamerican0402-100.

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28

Chast, Roz. "Fuzzy Logic." Scientific American 288, no. 4 (2003): 104. http://dx.doi.org/10.1038/scientificamerican0403-104.

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29

C, Swathi, Jenifer Ebienazer, Swathi M, and Suruthipriya S. "Fuzzy Logic." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 147–52. http://dx.doi.org/10.26562/ijiris.2023.v0903.19.

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Fuzzy logic is a mathematical framework for reasoning about ambiguous or inaccurate information. It is founded on the idea that truth can be stated as a degree of membership in a fuzzy set rather than as a binary value of true or untrue. Fuzzy logic is used in control systems, artificial intelligence, and decision-making. This paper defines fuzzy logic and discusses its key concepts, mathematical underpinnings, and applications. We look at the benefits and drawbacks.

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30

M., Bhuvaneswari, Daniel Samson P., and Anish V. "Fuzzy Logic." International Journal of Trend in Scientific Research and Development 4, no. 2 (2020): 884–87. https://doi.org/10.5281/zenodo.3843157.

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This paper proposes a detailed switching model for the medium voltage cascaded H bridge multi level inverter drive and induction motor system using fuzzy logic controller which is suitable for power system dynamic studies. The model includes the We describe in this book recent advances in the fuzzy logic based augmentation of neural networks and in optimization algorithms and their application in areas fuzzy logic can help design robust individual behaviours units. Fuzzy logic controllers incorporate heuristic control knowledge. It is convenient choice when a precise linear model of the system

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31

Meech, J. A. "Fuzzy Logic ' '93; 1st Annual Fuzzy Logic Symposium." Minerals Engineering 7, no. 1 (1994): 118–20. http://dx.doi.org/10.1016/0892-6875(94)90154-6.

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32

Zhang, Jinjin, Xiaoxia Zhou, Yan Zhang, and Lixing Tan. "Fuzzy Epistemic Logic: Fuzzy Logic of Doxastic Attitudes." Mathematics 13, no. 7 (2025): 1105. https://doi.org/10.3390/math13071105.

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In traditional epistemic logic—particularly modal logic—agents are often assumed to have complete and certain knowledge, which is unrealistic in real-world scenarios where uncertainty, imprecision, and the incompleteness of information are common. This study proposes an extension of the logic of doxastic attitudes to a fuzzy setting, representing beliefs or knowledge as continuous values in the interval [0, 1] rather than binary Boolean values. This approach offers a more nuanced and realistic modeling of belief states, capturing the inherent uncertainty and vagueness in human reasoning. We in

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33

Aldana, Milagrosa, and María Antonia Lledó. "The Fuzzy Bit." Symmetry 15, no. 12 (2023): 2103. http://dx.doi.org/10.3390/sym15122103.

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In this paper, the formulation of Quantum Mechanics in terms of fuzzy logic and fuzzy sets is explored. A result by Pykacz, which establishes a correspondence between (quantum) logics (lattices with certain properties) and certain families of fuzzy sets, is applied to the Birkhoff–von Neumann logic, the lattice of projectors of a Hilbert space. Three cases are considered: the qubit, two qubits entangled, and a qutrit ‘nested’ inside the two entangled qubits. The membership functions of the fuzzy sets are explicitly computed and all the connectives of the fuzzy sets are interpreted as operation

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34

TRILLAS, E. "ON LOGIC AND FUZZY LOGIC." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 01, no. 02 (1993): 107–37. http://dx.doi.org/10.1142/s0218488593000073.

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This paper mainly consists of a review of some basic tools of Inexact Inference, its reduction to classical logic and its cautious use of Fuzzy Logic. Those tools are the concept of Conditional Relation, its greatest case of Material Conditional and the concept of Logical-States as possible worlds of "true" elements. Some recent results characterizing Monotonic Preorders are also introduced, in both the Classical and Fuzzy cases. Everything lies on the semantic level of Logic and is presented in a naive mathematical style.

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35

Cong, Cuong Bui, Roan Thi Ngan, and Le Ba Long. "Some New De Morgan Picture Operator Triples in Picture Fuzzy Logic." Journal of Computer Science and Cybernetics 33, no. 2 (2018): 143–64. http://dx.doi.org/10.15625/1813-9663/33/2/10706.

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A new concept of picture fuzzy sets (PFS) were introduced in 2013, which are directextensions of the fuzzy sets and the intuitonistic fuzzy sets. Then some operations on PFS withsome properties are considered in [ 9,10 ]. Some basic operators of fuzzy logic as negation, tnorms, t-conorms for picture fuzzy sets firstly are defined and studied in [13,14]. This paper isdevoted to some classes of representable picture fuzzy t-norms and representable picture fuzzyt-conorms on PFS and a basic algebra structure of Picture Fuzzy Logic – De Morgan triples ofpicture operators.

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36

PUNČOCHÁŘ, VÍT. "SUBSTRUCTURAL INQUISITIVE LOGICS." Review of Symbolic Logic 12, no. 2 (2019): 296–330. http://dx.doi.org/10.1017/s1755020319000017.

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AbstractThis paper shows that any propositional logic that extends a basic substructural logic BSL (a weak, nondistributive, nonassociative, and noncommutative version of Full Lambek logic with a paraconsistent negation) can be enriched with questions in the style of inquisitive semantics and logic. We introduce a relational semantic framework for substructural logics that enables us to define the notion of an inquisitive extension of λ, denoted as ${\lambda ^?}$, for any logic λ that is at least as strong as BSL. A general theory of these “inquisitive extensions” is worked out. In particular,

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37

Lee, C. C. "Fuzzy logic in control systems: fuzzy logic controller. I." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 2 (1990): 404–18. http://dx.doi.org/10.1109/21.52551.

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38

Lee, C. C. "Fuzzy logic in control systems: fuzzy logic controller. II." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 2 (1990): 419–35. http://dx.doi.org/10.1109/21.52552.

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39

Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms

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40

Godo, L., J. Jacas, and L. Valverde. "Fuzzy values in fuzzy logic." International Journal of Intelligent Systems 6, no. 2 (1991): 199–212. http://dx.doi.org/10.1002/int.4550060207.

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41

buche, antje, jonas buche, and markus b. siewert. "fuzzy logic or fuzzy application? a response to Stockemer’s ‘fuzzy set or fuzzy logic?" European Political Science 15, no. 3 (2016): 359–78. http://dx.doi.org/10.1057/eps.2015.97.

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42

GAO, XIAOYU, Q. S. GAO, Y. HU, and L. LI. "A PROBABILITY-LIKE NEW FUZZY SET THEORY." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 03 (2006): 441–62. http://dx.doi.org/10.1142/s0218001406004697.

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In this paper, the reasons for the shortcoming of Zadeh's fuzzy set theory — it cannot correctly reflect different kinds of fuzzy phenomenon in the natural world — are discussed. In addition, the proof of the error of Zadeh's fuzzy set theory — it incorrectly defined the set complement that cannot exist in Zadeh's fuzzy set theory — is proposed. This error of Zadeh's fuzzy set theory causes confusion in thinking, logic and conception. It causes the seriously mistaken belief that logics of fuzzy sets necessarily go against classical and normal thinking, logic and conception. Two new fuzzy set t

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43

Guller, Dušan. "A Proof Calculus for Automated Deduction in Propositional Product Logic." Mathematics 12, no. 23 (2024): 3805. https://doi.org/10.3390/math12233805.

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Propositional product logic belongs to the basic fuzzy logics with continuous t-norms using the product t-norm (defined as the ordinary product of real numbers) on the unit interval [0,1]. This paper introduces a proof calculus for the product logic which is suitable for automated deduction. The calculus provides one of possible generalisations of the family of modifications of the procedure (algorithm) of Davis, Putnam, Logemann, and Loveland (DPLL) in the context of fuzzy logics. We show that the calculus is refutation sound and finitely complete as well. The deduction, satisfiability, and v

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44

Castro-Manzano, J. Martín. "Is Szabolcsi’s Logic a Fuzzy Logic?" Tópicos, Revista de Filosofía, no. 71 (December 17, 2024): 345–62. https://doi.org/10.21555/top.v710.2825.

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In this paper we ask ourselves whether Szabolcsi’s numerical term logic is a fuzzy logic. Our answer is in the affirmative. In order to justify such a claim, we first expound some preliminaries that help us understand why the inclusion of fuzzy quantifiers is a sufficient condition for fuzziness. Then we present Szabolcsi’s logic, which includes said quantifiers.

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45

PERLOVSKY, LEONID I. "FUZZY DYNAMIC LOGIC." New Mathematics and Natural Computation 02, no. 01 (2006): 43–55. http://dx.doi.org/10.1142/s1793005706000300.

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Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working o

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Muhammad Saqlain, Kashaf Naz, Kashf Gaffar, and Muhammad Naveed Jafar. "Fuzzy Logic Controller." Scientific Inquiry and Review 3, no. 3 (2019): 16–29. http://dx.doi.org/10.32350/sir.33.02.

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In this research paper, the impact of water pH on detergent was measured by constructing a Fuzzy Logic Controller (FLC) based on Intuitionistic Fuzzy Numbers (IFNs) by incorporating three linguistic inputs and one output as taken by Saeed. M. et al. [1]. The inference process was carried out using MATLAB fuzzy logic toolbox and the results were compared with FLC based on fuzzy numbers. The objective of the study was the comparison of FLC based on intuitionistic and fuzzy numbers. The results showed that FLC based on IFNs is approximately the same but has more precise values. So, IFNs based FLC

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47

Egesoy, Ahmet, and Aylin Güzel. "Fuzzy Logic Support for Requirements Engineering." International Journal of Innovative Research in Computer Science & Technology 9, no. 2 (2021): 14–21. http://dx.doi.org/10.21276/ijircst.2021.9.2.3.

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48

Heald, Graeme. "Issues with Reliability of Fuzzy Logic." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 829–34. http://dx.doi.org/10.31142/ijtsrd18573.

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49

Mataram, I. Made. "FUZZY LOGIC STATIC SYNCHRONOUS COMPENSATOR (FLSTATCOM)." Majalah Ilmiah Teknologi Elektro 15, no. 1 (2016): 34–37. http://dx.doi.org/10.24843/mite.1501.06.

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U. Kulkarni, Akshay, Amit M. Potadar, Amogh R. Gaonkar, and Amresh Kumar. "Fuzzy Logic Based Career Guidance System." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 01–04. http://dx.doi.org/10.9756/bijsesc.8230.

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