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Zeitschriftenartikel zum Thema „Fuzzy logic“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 31. Juli 2024

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1

BOBILLO, FERNANDO, MIGUEL DELGADO, JUAN GÓMEZ-ROMERO und UMBERTO STRACCIA. „JOINING GÖDEL AND ZADEH FUZZY LOGICS IN FUZZY DESCRIPTION LOGICS“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, Nr. 04 (August 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 OWL 2 — that joins Gödel and Zadeh fuzzy logics. We analyze the properties of the new fuzzy Description Logic in order to provide guidelines to ontology developers to exploit the best features of each fuzzy logic. The proposal also considers degrees of truth belonging to a finite set of linguistic terms rather than numerical values, thus being closer to real experts' reasonings. We prove the decidability of the combined logic by presenting a reasoning preserving procedure to obtain a crisp representation for it. This result is generalized to offer a similar reduction that can be applied when any other finite t -norms, t -conorms, negations or implications are considered in the logic.

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Metcalfe, George, und Franco Montagna. „Substructural fuzzy logics“. Journal of Symbolic Logic 72, Nr. 3 (September 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 lattices. Gentzen systems admitting cut-elimination are given in the framework of hypersequents. Completeness with respect to algebras with lattice reduct [0, 1] is established for UL and several extensions using a two-part strategy. First, completeness is proved for the logic extended with Takeuti and Titani's density rule. A syntactic elimination of the rule is then given using a hypersequent calculus. As an algebraic corollary, it follows that certain varieties of residuated lattices are generated by their members with lattice reduct [0, 1].

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Gehrke, Mai, Carol Walker und Elbert Walker. „A Mathematical Setting for Fuzzy Logics“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, Nr. 03 (Juni 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 with component-wise operations, is propositional interval-valued fuzzy logic. This is shown to be the same as the logic given by a certain four element lattice of truth values. Since both of these logics are equivalent to ones given by finite algebras, it follows that there are finite algorithms for determining when two statements are logically equivalent within either of these logics. On this topic, normal forms are discussed for both of these logics.

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4

MIYAKOSHI, Masaaki. „Fuzzy Logic“. Journal of Japan Society for Fuzzy Theory and Systems 4, Nr. 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, Nr. 2 (August 2003): 96. http://dx.doi.org/10.1038/scientificamerican0803-96.

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6

Chast, Roz. „Fuzzy Logic“. Scientific American 288, Nr. 6 (Juni 2003): 92. http://dx.doi.org/10.1038/scientificamerican0603-92.

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7

Klir, G. J. „Fuzzy logic“. IEEE Potentials 14, Nr. 4 (1995): 10–15. http://dx.doi.org/10.1109/45.468220.

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8

Chast, Roz. „Fuzzy Logic“. Scientific American 288, Nr. 3 (März 2003): 112. http://dx.doi.org/10.1038/scientificamerican0303-112.

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9

Chast, Roz. „Fuzzy Logic“. Scientific American 287, Nr. 6 (Dezember 2002): 139. http://dx.doi.org/10.1038/scientificamerican1202-139.

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10

Chast, Roz. „Fuzzy Logic“. Scientific American 289, Nr. 6 (Dezember 2003): 128. http://dx.doi.org/10.1038/scientificamerican1203-128.

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11

Chast, Roz. „Fuzzy Logic“. Scientific American 286, Nr. 5 (Mai 2002): 104. http://dx.doi.org/10.1038/scientificamerican0502-104.

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12

Kosko, Bart, und Satoru Isaka. „Fuzzy Logic“. Scientific American 269, Nr. 1 (Juli 1993): 76–81. http://dx.doi.org/10.1038/scientificamerican0793-76.

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13

Chast, Roz. „Fuzzy Logic“. Scientific American 287, Nr. 2 (August 2002): 96. http://dx.doi.org/10.1038/scientificamerican0802-96.

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14

Chast, Roz. „Fuzzy Logic“. Scientific American 290, Nr. 2 (Februar 2004): 100. http://dx.doi.org/10.1038/scientificamerican0204-100.

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15

Chast, Roz. „Fuzzy Logic“. Scientific American 287, Nr. 3 (September 2002): 104. http://dx.doi.org/10.1038/scientificamerican0902-104.

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16

Chast, Roz. „Fuzzy Logic“. Scientific American 289, Nr. 3 (September 2003): 116. http://dx.doi.org/10.1038/scientificamerican0903-116.

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17

Chast, Roz. „Fuzzy Logic“. Scientific American 287, Nr. 5 (November 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, Nr. 1 (Oktober 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, Nr. 1 (März 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, Nr. 10 (Oktober 2002): 724. http://dx.doi.org/10.1038/nrc919.

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21

Zadeh, Lotfi. „Fuzzy logic“. Scholarpedia 3, Nr. 3 (2008): 1766. http://dx.doi.org/10.4249/scholarpedia.1766.

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22

Chast, Roz. „Fuzzy Logic“. Scientific American 290, Nr. 1 (Januar 2004): 110. http://dx.doi.org/10.1038/scientificamerican0104-110.

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23

Zadeh, L. A. „Fuzzy logic“. Computer 21, Nr. 4 (April 1988): 83–93. http://dx.doi.org/10.1109/2.53.

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24

Chast, Roz. „Fuzzy Logic“. Scientific American 288, Nr. 2 (Februar 2003): 94. http://dx.doi.org/10.1038/scientificamerican0203-94.

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25

Chast, Roz. „Fuzzy Logic“. Scientific American 287, Nr. 4 (Oktober 2002): 108. http://dx.doi.org/10.1038/scientificamerican1002-108.

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26

Chast, Roz. „Fuzzy Logic“. Scientific American 289, Nr. 4 (Oktober 2003): 104. http://dx.doi.org/10.1038/scientificamerican1003-104.

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27

Chast, Roz. „Fuzzy Logic“. Scientific American 286, Nr. 4 (April 2002): 100. http://dx.doi.org/10.1038/scientificamerican0402-100.

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28

Chast, Roz. „Fuzzy Logic“. Scientific American 288, Nr. 4 (April 2003): 104. http://dx.doi.org/10.1038/scientificamerican0403-104.

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29

C, Swathi, Jenifer Ebienazer, Swathi M und Suruthipriya S. „Fuzzy Logic“. International Journal of Innovative Research in Information Security 09, Nr. 03 (23.06.2023): 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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Meech, J. A. „Fuzzy Logic ' '93; 1st Annual Fuzzy Logic Symposium“. Minerals Engineering 7, Nr. 1 (Januar 1994): 118–20. http://dx.doi.org/10.1016/0892-6875(94)90154-6.

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31

Aldana, Milagrosa, und María Antonia Lledó. „The Fuzzy Bit“. Symmetry 15, Nr. 12 (23.11.2023): 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 operations with these particular membership functions. In this way, a complete picture of the standard quantum logic in terms of fuzzy sets is obtained for the systems considered.

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32

TRILLAS, E. „ON LOGIC AND FUZZY LOGIC“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 01, Nr. 02 (Dezember 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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33

Lee, C. C. „Fuzzy logic in control systems: fuzzy logic controller. I“. IEEE Transactions on Systems, Man, and Cybernetics 20, Nr. 2 (1990): 404–18. http://dx.doi.org/10.1109/21.52551.

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34

Lee, C. C. „Fuzzy logic in control systems: fuzzy logic controller. II“. IEEE Transactions on Systems, Man, and Cybernetics 20, Nr. 2 (1990): 419–35. http://dx.doi.org/10.1109/21.52552.

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35

Godo, L., J. Jacas und L. Valverde. „Fuzzy values in fuzzy logic“. International Journal of Intelligent Systems 6, Nr. 2 (März 1991): 199–212. http://dx.doi.org/10.1002/int.4550060207.

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36

buche, antje, jonas buche und markus b. siewert. „fuzzy logic or fuzzy application? a response to Stockemer’s ‘fuzzy set or fuzzy logic?“ European Political Science 15, Nr. 3 (10.08.2016): 359–78. http://dx.doi.org/10.1057/eps.2015.97.

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37

PUNČOCHÁŘ, VÍT. „SUBSTRUCTURAL INQUISITIVE LOGICS“. Review of Symbolic Logic 12, Nr. 2 (01.02.2019): 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, it is shown how to axiomatize ${\lambda ^?}$, given the axiomatization of λ. Furthermore, the general theory is applied to some prominent logical systems in the class: classical logic Cl, intuitionistic logic Int, and t-norm based fuzzy logics, including for example Łukasiewicz fuzzy logic Ł. For the inquisitive extensions of these logics, axiomatization is provided and a suitable semantics found.

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38

Cong, Cuong Bui, Roan Thi Ngan und Le Ba Long. „Some New De Morgan Picture Operator Triples in Picture Fuzzy Logic“. Journal of Computer Science and Cybernetics 33, Nr. 2 (03.01.2018): 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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39

Egesoy, Ahmet, und Aylin Güzel. „Fuzzy Logic Support for Requirements Engineering“. International Journal of Innovative Research in Computer Science & Technology 9, Nr. 2 (März 2021): 14–21. http://dx.doi.org/10.21276/ijircst.2021.9.2.3.

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40

Heald, Graeme. „Issues with Reliability of Fuzzy Logic“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (31.10.2018): 829–34. http://dx.doi.org/10.31142/ijtsrd18573.

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41

Mataram, I. Made. „FUZZY LOGIC STATIC SYNCHRONOUS COMPENSATOR (FLSTATCOM)“. Majalah Ilmiah Teknologi Elektro 15, Nr. 1 (15.06.2016): 34–37. http://dx.doi.org/10.24843/mite.1501.06.

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42

U. Kulkarni, Akshay, Amit M. Potadar, Amogh R. Gaonkar und Amresh Kumar. „Fuzzy Logic Based Career Guidance System“. Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (31.10.2016): 01–04. http://dx.doi.org/10.9756/bijsesc.8230.

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43

Raj, A. Stanley, D. Hudson Oliver und 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 of fuzzy logic, namely, fuzzyC-means clustering, fuzzyK-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.

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44

PERLOVSKY, LEONID I. „FUZZY DYNAMIC LOGIC“. New Mathematics and Natural Computation 02, Nr. 01 (März 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 of the mind.

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45

Muhammad Saqlain, Kashaf Naz, Kashf Gaffar und Muhammad Naveed Jafar. „Fuzzy Logic Controller“. Scientific Inquiry and Review 3, Nr. 3 (20.09.2019): 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 can be used in the Instinctive Laundry System.

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46

GAO, XIAOYU, Q. S. GAO, Y. HU und L. LI. „A PROBABILITY-LIKE NEW FUZZY SET THEORY“. International Journal of Pattern Recognition and Artificial Intelligence 20, Nr. 03 (Mai 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 theories, C-fuzzy set theory and probability-like fuzzy set theory, the N-fuzzy set theory, are proposed. The two are equivalent, and both overcome the error and shortcoming of Zadeh's fuzzy set theory, and they are consistent with normal, natural and classical thinking, logic and concepts. The similarities of N-fuzzy set theory with probability theory are also examined.

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Hayward, Gordon, und Valerie Davidson. „Fuzzy logic applications“. Analyst 128, Nr. 11 (2003): 1304. http://dx.doi.org/10.1039/b312701j.

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48

Costa, A., A. De Gloria, F. Giudici und M. Olivieri. „Fuzzy logic microcontroller“. IEEE Micro 17, Nr. 1 (1997): 66–74. http://dx.doi.org/10.1109/40.566209.

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49

Vojtáš, Peter. „Fuzzy logic programming“. Fuzzy Sets and Systems 124, Nr. 3 (Dezember 2001): 361–70. http://dx.doi.org/10.1016/s0165-0114(01)00106-3.

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50

Buckley, J. J., und W. Siler. „L∞ fuzzy logic“. Fuzzy Sets and Systems 107, Nr. 3 (November 1999): 309–22. http://dx.doi.org/10.1016/s0165-0114(97)00294-7.

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