Relevant bibliographies by topics / Fuzzy sets

Academic literature on the topic 'Fuzzy sets'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Fuzzy sets"

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Thakur, S. S., and Annamma Philip. "Pairwise fuzzy connectedness between fuzzy sets." Mathematica Bohemica 122, no. 4 (1997): 375–80. http://dx.doi.org/10.21136/mb.1997.126217.

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Wang, Guo-jun, and Ying-Yu He. "Intuitionistic fuzzy sets and -fuzzy sets." Fuzzy Sets and Systems 110, no. 2 (March 2000): 271–74. http://dx.doi.org/10.1016/s0165-0114(98)00011-6.

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Lee, Seok Jong, and Eun Pyo Lee. "Fuzzyr-continuous and fuzzyr-semicontinuous maps." International Journal of Mathematics and Mathematical Sciences 27, no. 1 (2001): 53–63. http://dx.doi.org/10.1155/s0161171201010882.

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We introduce a new notion of fuzzyr-interior which is an extension of Chang's fuzzy interior. Using fuzzyr-interior, we define fuzzyr-semiopen sets and fuzzyr-semicontinuous maps which are generalizations of fuzzy semiopen sets and fuzzy semicontinuous maps in Chang's fuzzy topology, respectively. Some basic properties of fuzzyr-semiopen sets and fuzzyr-semicontinuous maps are investigated.
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Tuan, Han-Wen, and Henry Chung-Jen Chao. "Non–fuzzy sets for intuitionistic fuzzy sets." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 7-8 (November 17, 2018): 1509–14. http://dx.doi.org/10.1080/09720529.2017.1367467.

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Shi, Fu-Gui, and Chao-Zan Fan. "Fuzzy soft sets as L-fuzzy sets." Journal of Intelligent & Fuzzy Systems 37, no. 4 (October 25, 2019): 5061–66. http://dx.doi.org/10.3233/jifs-182828.

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DUBOIS, DIDIER, and HENRI PRADE. "ROUGH FUZZY SETS AND FUZZY ROUGH SETS*." International Journal of General Systems 17, no. 2-3 (June 1990): 191–209. http://dx.doi.org/10.1080/03081079008935107.

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Mukherjee, M. N., and S. P. Sinha. "Fuzzyθ-closure operator on fuzzy topological spaces." International Journal of Mathematics and Mathematical Sciences 14, no. 2 (1991): 309–14. http://dx.doi.org/10.1155/s0161171291000364.

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The paper contains a study of fuzzyθ-closure operator,θ-closures of fuzzy sets in a fuzzy topological space are characterized and some of their properties along with their relation with fuzzyδ-closures are investigated. As applications of these concepts, certain functions as well as some spaces satisfying certain fuzzy separation axioms are characterized in terms of fuzzyθ-closures andδ-closures.
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DEMİRCİ, MUSTAFA. "GENUINE SETS, VARIOUS KINDS OF FUZZY SETS AND FUZZY ROUGH SETS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 04 (August 2003): 467–94. http://dx.doi.org/10.1142/s0218488503002193.

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In this paper, deriving the type-m fuzzy sets, intuitionistic fuzzy sets, Φ-fuzzy sets, rough sets, fuzzy rough sets and rough fuzzy sets as particular genuine sets, and establishing their connections with genuine sets, it is demonstrated that the theory of genuine sets provides a powerful tool to model various different kinds of uncertainty in a mathematical way. Furthermore, it is also shown that the genuine set theoretic descriptions of type-m fuzzy sets, intuitionistic fuzzy sets and fuzzy rough sets point out new features of these set notations, originated from the peculiar characteristics of genuine sets.
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Mohite, Trupti. "On Convexity of Fuzzy Sets and Fuzzy Relations." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 758–61. http://dx.doi.org/10.21275/sr23710111006.

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Mu, Beining. "Fuzzy Julia Sets and Fuzzy Superior Julia Sets." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 375–80. http://dx.doi.org/10.54097/5c5hp748.

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This article examine the past study of fuzzy Mandelbrot set and fuzzy superior Mandelbrot set, then give the definition of fuzzy Julia sets and fuzzy superior Julia sets with the idea of utilizing membership functions to represent the escape velocity of Julia sets or superior Julia sets of each complex number in the definition of fuzzy Mandelbrot set and fuzzy superior Mandelbrot set inherited while the membership functions are selected to distinguish complex numbers with different escape velocity and same orbit. Then some examples of fuzzy Julia sets and fuzzy superior Julia sets are presented. With some observation of the examples, some analytical and topological properties of fuzzy Julia sets are demonstrated and sketches of the proofs are also presented. In the part of fuzzy superior Julia sets, some trivial conclusions as well as an example is presented where more complicated properties which involves the study into very chaotic behaviors are left open.
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Dissertations / Theses on the topic "Fuzzy sets"

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Naman, Saleem Muhammad. "Eigen Fuzzy Sets of Fuzzy Relation with Applications." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4060.

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Eigen fuzzy sets of fuzzy relation can be used for the estimation of highest and lowest levels of involved variables when applying max-min composition on fuzzy relations. By the greatest eigen fuzzy sets (set which can be greater anymore) maximum membership degrees of any fuzzy set can be found, with the help of least eigen fuzzy set (set which can be less anymore) minimum membership degrees of any fuzzy sets can be found as well.The lowest and highest level, impact or e ffect of anything can be found by applying eigen fuzzy set theory. The implicational aspect of this research study is medical and customer satisfaction level measurement. By applying methods of eigen fuzzy set theory the e ffectiveness of medical cure and customer satisfaction can be found with high precision.
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Rabetge, Christian. "Fuzzy Sets in der Netzplantechnik /." Wiesbaden : Dt. Univ.-Verl, 1991. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=002624347&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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VISA, SOFIA. "FUZZY CLASSIFIERS FOR IMBALANCED DATA SETS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1182226868.

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Meyer, David, and Kurt Hornik. "Generalized and Customizable Sets in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/1062/1/document.pdf.

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We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Hornik, Kurt, and David Meyer. "Generalized and Customizable Sets in R." American Statistical Association, 2009. http://epub.wu.ac.at/4002/1/sets.pdf.

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We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (authors' abstract)
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Li, Ying. "Probabilistic interpretations of fuzzy sets and systems." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11619.

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John, Robert. "Perception modelling using type-2 fuzzy sets." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/5856.

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Type-1 fuzzy logic has, for over thirty years, provided an approach for modelling uncertainty and imprecision. This methodology has been highly successful with a history of successful applications in a number of areas - particularly control. However, type-1 fuzzy systems are essentially `crisp' in nature. This is not only paradoxical but also raises concerns for knowledge representation and inferencing. In particular type-1 fuzzy logic is flawed when representing perceptions such as colour, beauty, comfort etc. since these perceptions do not have a measurable domain. This fundamental paradox is tackled in this research by employing a type-2 fuzzy paradigm. The investigation of the type-2 approach concludes that the uncertainty or imprecision that exists in most real problems can be more effectively modelled by a type-2 approach. The research reported in this thesis explores the properties of type-2 fuzzy sets as well as showing how useful they can be for knowledge representation and inferencing. It is shown that type-2 fuzzy sets have an important role to play in modelling perceptions. Results are given of using type-2 fuzzy sets to represent perceptions of a medical expert for shin image analysis indicating that the type-2 fuzzy paradigm is particularly helpful for perception representation. A methodology has been developed that allows linguistic inputs to an adaptive system that implements a type-2 fuzzy system(the Adaptive Fuzzy Perception Learner (AFPL)). In this thesis, the rationale and full mathematical detail of the AFPL is presented. The approach has been applied successfully to the, so called, linguistic AND (analogous to the Boolean AND) as an aid to illustrating the methodology. Results are presented of applying the method to a real problem of classifying the acceptability of a car based on perceptions that describe certain features of the car. The AFPL is applied to this large, complex, set of data where the inputs to the network are linguistic. A detailed evaluation of the AFPL is given with recommendations for effective use of the AFPL. The results indicate that we now, truly, have an approach for learning the perceptions and relations in a type-2 fuzzy system
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Mahlasela, Zuko. "Finite fuzzy sets, keychains and their applications." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005220.

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The idea of keychains, an (n+1)-tuple of non-increasing real numbers in the unit interval always including 1, naturally arises in study of finite fuzzy set theory. They are a useful concept in modeling ideas of uncertainty especially those that arise in Economics, Social Sciences, Statistics and other subjects. In this thesis we define and study some basic properties of keychains with reference to Partially Ordered Sets, Lattices, Chains and Finite Fuzzy Sets. We then examine the role of keychains and their lattice diagrams in representing uncertainties that arise in such problems as in preferential voting patterns, outcomes of competitions and in Economics - Preference Relations.
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Palancioglu, Haci Mustafa. "Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/PalanciogluHM2003.pdf.

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Jensen, Richard. "Combining rough and fuzzy sets for feature selection." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/24740.

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Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. FS techniques have also been applied to small and medium-sized datasets in order to locate the most informative features for later use. Many feature selection methods have been developed and are reviewed critically in this thesis, with particular emphasis on their current limitations. The leading methods in this field are presented in a consistent algorithmic framework. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in FS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based feature selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This thesis proposes and develops an approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. Complexity analysis of the underlying algorithms is included. FRFS is applied to two domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other FS techniques in the comparative study. Based on the new fuzzy-rough measure of feature significance^ further develop­ment of the FRFS technique is presented in this thesis. This is developed from the new area of feature grouping that considers the selection of groups of attributes in the search for the best subset. A novel framework is also given for the application of ant-based search mechanisms within feature selection in general, with particular emphasis on its employment in FRFS. Both of these developments are employed and evaluated within the complex systems monitoring application.
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Books on the topic "Fuzzy sets"

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Gottwald, Siegfried. Fuzzy Sets and Fuzzy Logic. Wiesbaden: Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-86812-1.

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Maria, Bojadziev, ed. Fuzzy sets, fuzzy logic, applications. Singapore: World Scientific Pub. Co., 1995.

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Garg, Harish, ed. Pythagorean Fuzzy Sets. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1989-2.

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Atanassov, Krassimir T. Intuitionistic Fuzzy Sets. Heidelberg: Physica-Verlag HD, 1999. http://dx.doi.org/10.1007/978-3-7908-1870-3.

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Li, Hong-Xing. Fuzzy sets and fuzzy decision-making. Boca Raton: CRC Press, 1995.

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Siegfried, Gottwald, ed. Fuzzy sets, fuzzy logic, fuzzy methods with applications. Chichester: J. Wiley, 1995.

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1932-, Klir George J., and Yuan Bo, eds. Fuzzy sets, fuzzy logic, and fuzzy systems: Selected papers. Singapore: World Scientific, 1996.

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Xu, Zeshui. Hesitant Fuzzy Sets Theory. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04711-9.

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Wygralak, Maciej. Cardinalities of Fuzzy Sets. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36382-8.

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Höhle, Ulrich, and Stephen Ernest Rodabaugh, eds. Mathematics of Fuzzy Sets. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5079-2.

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Book chapters on the topic "Fuzzy sets"

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Buckley, James J. "Fuzzy Sets." In Fuzzy Probabilities, 7–30. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-86786-6_2.

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Buckley, James J. "Fuzzy Sets." In Fuzzy Statistics, 5–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_2.

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Bede, Barnabas. "Fuzzy Sets." In Mathematics of Fuzzy Sets and Fuzzy Logic, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_1.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Fuzzy Sets." In Data Mining Methods for Knowledge Discovery, 73–129. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_3.

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Buckley, James J., and Leonard J. Jowers. "Fuzzy Sets." In Monte Carlo Methods in Fuzzy Optimization, 9–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76290-4_2.

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Ramík, Jaroslav, and Milan Vlach. "Fuzzy Sets." In Generalized Concavity in Fuzzy Optimization and Decision Analysis, 121–57. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1485-5_6.

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Lowen, R. "Fuzzy Sets." In Fuzzy Set Theory, 21–47. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8741-9_2.

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Aliev, Rafik Aziz, and Babek Ghalib Guirimov. "Fuzzy Sets." In Type-2 Fuzzy Neural Networks and Their Applications, 1–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09072-6_1.

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Allahviranloo, Tofigh. "Fuzzy Sets." In Fuzzy Fractional Differential Operators and Equations, 7–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51272-9_2.

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Miyamoto, Sadaaki. "Fuzzy Sets." In Fuzzy Sets in Information Retrieval and Cluster Analysis, 7–44. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7887-5_2.

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Conference papers on the topic "Fuzzy sets"

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Klement, Erich Peter, Radko Mesiar, and Andrea Stupnanova. "Picture fuzzy sets and 3-fuzzy sets." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491520.

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Resconi, G., and C. J. Hinde. "Active sets, fuzzy sets and inconsistency." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584090.

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Venkata Subba Reddy, P. "Generalization of fuzzy sets type-2, fuzzy quantifiers sets and α-cut fuzzy sets fuzzy temporal sets, fuzzy granular sets and fuzzy rough sets for incomplete information." In 2014 International Conference on Fuzzy Theory and Its Applications (iFuzzy). IEEE, 2014. http://dx.doi.org/10.1109/ifuzzy.2014.7091236.

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Walker, Carol L., Elbert A. Walker, and Ronald R. Yager. "Some comments on level sets of fuzzy sets." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630527.

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Yuan, Xue-hai, Hong-xing Li, and Cheng Zhang. "Fuzzy Probabilistic Sets and r-Fuzzy Sets." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.100.

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Shokeen, Jyoti, and Chhavi Rana. "Fuzzy sets, advanced fuzzy sets and hybrids." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389911.

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Baccour, Leila, and Adel M. Alimi. "Distance Measures for Intuitionistic Fuzzy Sets and Interval Valued Intuitionistic Fuzzy Sets." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858789.

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Gegeny, David, and Szilveszter Kovacs. "Fuzzy Interpolation of Fuzzy Rough Sets." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882837.

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Matusiewicz, Zofia, and Władysław Homenda. "Operations on Balanced Fuzzy Sets." In 2023 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2023. http://dx.doi.org/10.1109/fuzz52849.2023.10309700.

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Hsiao, Chih-Ching. "Robust function approximation based on fuzzy sets and rough sets." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277427.

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Reports on the topic "Fuzzy sets"

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Atanasso, Krassimir. Elliptic Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2021. http://dx.doi.org/10.7546/crabs.2021.06.02.

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Ismaili, Shpend, and Stefka Fidanova. Application of Intuitionistic Fuzzy Sets on Agent Based Modelling. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2018. http://dx.doi.org/10.7546/crabs.2018.06.12.

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Atanassov, Krassimir. A New Modal Type Operator over Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2020. http://dx.doi.org/10.7546/crabs.2020.08.01.

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Goodman, I. R. Applications of Random Set Representations of Fuzzy Sets to Determining Measures of Central Tendency. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada305661.

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Andonyadis, Manol, A. Altschaeffl, and Jean-Lou Chameau. The Use of Fuzzy Sets Mathematics to Assist Pavement Evaluation and Management. West Lafayette, IN: Purdue University, 1985. http://dx.doi.org/10.5703/1288284314099.

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Darby, John L. LinguisticBelief: a java application for linguistic evaluation using belief, fuzzy sets, and approximate reasoning. Office of Scientific and Technical Information (OSTI), March 2007. http://dx.doi.org/10.2172/903427.

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Andonyadis, Manol, A. Altschaeffl, and Jean-Lou Chameau. The Use of Fuzzy Sets Mathematics to Assist Pavement Evaluation and Management: Executive Summary. West Lafayette, IN: Purdue University, 1986. http://dx.doi.org/10.5703/1288284314098.

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Darby, John L. Evaluation of risk from acts of terrorism :the adversary/defender model using belief and fuzzy sets. Office of Scientific and Technical Information (OSTI), September 2006. http://dx.doi.org/10.2172/893554.

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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Gluckman, Albert G., and Aivars Celmins. Cost Effectiveness Analysis Using Fuzzy Set Theory. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada274003.

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