Relevant bibliographies by topics / Fuzzy Set. Empty Fuzzy Set

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy Set. Empty Fuzzy Set'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy Set. Empty Fuzzy Set"

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Kishore, Parimi Radhakrishna, and Sileshe Gone Korma. "FUZZY GENERALISED LATTICEs (fuzzy gls) Based on fuzzy partial ordering relation (fuzzy porel)." Journal of Scientific Research 68, no. 01 (2024): 39–44. http://dx.doi.org/10.37398/jsr.2024.680105.

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A generalised lattice is an intermediate structure between directed poset and lattice. Every generalised lattice is a directed poset but the converse is not true. Every lattice is a generalised lattice but the converse is not true. A fuzzy relation defined on a non-empty set is called a fuzzy partial ordered relation if it satisfies reflexive, anti-symmetric and transitive. A non-empty set together with a fuzzy partial ordered relation is called a fuzzy poset. This article deals with the concept of FUZZY GENERALISED LATTICE (fuzzy gl) Based on fuzzy partial ordering relation (fuzzy porel).
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Madani, Ahmad, Saman Abdurrahman, and Na'imah Hijriati. "RELASI FUZZY PADA GRUP FAKTOR FUZZY." JURNAL MATEMATIKA MURNI DAN TERAPAN EPSILON 14, no. 1 (2020): 33. http://dx.doi.org/10.20527/epsilon.v14i1.2394.

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Fuzzy subsets on the non-empty set is a mapping of this set to the interval . The concept of fuzzy subgroups introduced from advanced concept of fuzzy set in group theory. In concept of fuzzy set there is the concept of relations is fuzzy relations. In this study examined that fuzzy relations related to the equivalence and congruence on a fuzzy group and fuzzy factor group. The results of this study was to show that a fuzzy relation if and if is a fuzzy congruence relations on fuzzy group and a fuzzy relation defined of is a fuzzy congruence relations on fuzzy factor group.
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Rajbongshi, Dr Sisir Kumar, and Prasanta Gogoi. "On Some Theoretic Aspects of Fuzzy Subsets." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 29–33. http://dx.doi.org/10.22214/ijraset.2023.48934.

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Abstract: This article proposes some important theoretic aspects of L -fuzzy power set of a finite set. Here, we take a non-empty finite set E and an ordered subset M of the closed interval 0,1, then the set of mappings from E to M denoted by F is defined as L -fuzzy power set. Considering the disjunctive union ‘ ’ operations between fuzzy subsets of F , the structure F,forms a groupoid, which is defined as special fuzzy groupoid. Later, we try to introduce the product of special fuzzy groupoids and their properties
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S., Subramanian, and Muruganandham K. "MEASURES AND DETERMINATIONS OF SANCHEZ'S METHOD OVER DOUBLE-FRAMED FUZZY SOFT SETS." International Journal of Applied and Advanced Scientific Research 2, no. 2 (2017): 128–32. https://doi.org/10.5281/zenodo.887930.

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In our work, we have defined different types of double-framed fuzzy soft matrix. Operations of addition, multiplication and complement of double-framed fuzzy soft matrix are defined with examples. Some related propositions with proof is also worked out. Finally medical diagnosis problems have been solved using double framed fuzzy soft matrix.
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Georgiou, Dimitrios, and Georgios Prinos. "A New Notion of Fuzzy Function Ideal Convergence." Mathematics 12, no. 2 (2024): 260. http://dx.doi.org/10.3390/math12020260.

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P.M. Pu and Y.M. Liu extended Moore-Smith’s convergence of nets to fuzzy topology and Y.M. Liu provided analogous results to J. Kelley’s classical characterization theorem of net convergence by introducing the notion of fuzzy convergence classes. In a previous paper, the authors of this study provided modified versions of this characterization by using an alternative notion of convergence of fuzzy nets, introduced by B.M.U. Afsan, named fuzzy net ideal convergence. Our main scope here is to generalize and simplify the preceding results. Specifically, we insert the concept of a fuzzy function i
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Tchikapa, Cyrille Nganteu. "Anti Fuzzy Deductive Systems of BL-Algebras." International Journal of Artificial Life Research 3, no. 3 (2012): 32–40. http://dx.doi.org/10.4018/jalr.2012070103.

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The aim of this paper is to introduce the notion of anti fuzzy (prime) deductive system in BL-algebra and to investigate their properties. It is shown that the set of all deductive systems (with the empty set) of a BL-algebra X is equipotent to a quotient of the set of all anti fuzzy deductive systems of X. The anti fuzzy prime deductive system theorem of BL-algebras is also proved.
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Kazancı, Osman, Sarka Hoskova-Mayerova, and Bijan Davvaz. "Algebraic Hyperstructure of Multi-Fuzzy Soft Sets Related to Polygroups." Mathematics 10, no. 13 (2022): 2178. http://dx.doi.org/10.3390/math10132178.

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The combination of two elements in a group structure is an element, while, in a hypergroup, the combination of two elements is a non-empty set. The use of hypergroups appears mainly in certain subclasses. For instance, polygroups, which are a special subcategory of hypergroups, are used in many branches of mathematics and basic sciences. On the other hand, in a multi-fuzzy set, an element of a universal set may occur more than once with possibly the same or different membership values. A soft set over a universal set is a mapping from parameters to the family of subsets of the universal set. I
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Şahin, Mehmet, Necati Olgun, F. Talay Akyıldız, and Ali Karakuş. "Generalized Caratheodory Extension Theorem on Fuzzy Measure Space." Abstract and Applied Analysis 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/260457.

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Lattice-valued fuzzy measures are lattice-valued set functions which assign the bottom element of the lattice to the empty set and the top element of the lattice to the entire universe, satisfying the additive properties and the property of monotonicity. In this paper, we use the lattice-valued fuzzy measures and outer measure definitions and generalize the Caratheodory extension theorem for lattice-valued fuzzy measures.
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Chen, Chih-Yuan, Cheng-Pin Wang, and Tetz C. Huang. "A Characterization of Convex Fuzzy Mappings." International Journal of Artificial Life Research 1, no. 3 (2010): 40–44. http://dx.doi.org/10.4018/jalr.2010070104.

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As any real-valued functions can be regarded as a fuzzy mapping, the research on fuzzy mappings extends the research on real-valued functions. In this paper, a characterization of convex fuzzy mappings is obtained. Supposeis a fuzzy mapping, whereis a non-empty convex subset ofandis the set of all fuzzy numbers. With respect to the fuzzy-max order, is convex if and only if it is both quasi-convex and intermediate-point convex.
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J. Anusha, Dr. B. Uma Devi, R. K. Shanmugha Priya. "A New Approach on Bipolar Second Degree Perfect Fuzzy Matching for Bipolar Fuzzy Graphs Based on Vertices." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 4615–25. http://dx.doi.org/10.52783/tjjpt.v44.i3.2608.

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Graph is a visual representation of data. In mathematics, graph is a structure which creates the pairwise relation between objects. The objects are known as nodes or vertices and the connection between two nodes are known as links or edges. A graph is a symmetric binary relation on a non-empty set whereas Fuzzy graph is a symmetric binary fuzzy relation on a fuzzy subset. Bipolar Fuzzy graph is a generalization of graph theory by using bipolar Fuzzy set. The bipolar Fuzzy sets are the extension of Fuzzy sets. Our main objective is to introduce Bipolar Second Degree Perfect Fuzzy Matching for B
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Dissertations / Theses on the topic "Fuzzy Set. Empty Fuzzy Set"

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Mehmood, Rashid. "Fuzzy linear programming problems solved with Fuzzy decisive set method." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1201.

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In the thesis, there are two kinds of fuzzy linear programming problems, one of them is a linear programming problem with fuzzy technological coefficients and the second is linear programming problem in which both the right-hand side and the technological coefficients are fuzzy numbers. I solve the fuzzy linear programming problems with fuzzy decisive set method.
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Patel, Chintan. "Evaluating Trench Safety Using Fuzzy Logic Concept and Fuzzy Set Models." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1419353000.

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Mao, Hongwei. "Estimating labour productivity using fuzzy set theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0019/MQ47065.pdf.

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Randon, Nicholas John. "Fuzzy and random set based induction algorithms." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399941.

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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 medica
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Ornelas, Gilbert. "Set-valued extensions of fuzzy logic classification theorems /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Raghfar, Hossein. "Application of fuzzy set theory to poverty analysis." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343582.

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Saboo, Jai Vardhan. "An investment analysis model using fuzzy set theory." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/50087.

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Traditional methods for evaluating investments in state-of-the-art technology are sometimes found lacking in providing equitable recommendations for project selection. The major cause for this is the inability of these methods to handle adequately uncertainty and imprecision, and account for every aspect of the project, economic and non-economic, tangible and intangible. Fuzzy set theory provides an alternative to probability theory for handling uncertainty, while at the same time being able to handle imprecision. It also provides a means of closing the gap between the human thought process an
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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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Zyl, Jacobus van. "Fuzzy set covering as a new paradigm for the induction of fuzzy classification rules." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=984768548.

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Books on the topic "Fuzzy Set. Empty Fuzzy Set"

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Farhadinia, Bahram. Hesitant Fuzzy Set. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7301-6.

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Lowen, R. Fuzzy Set Theory. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8741-9.

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Smithson, Michael, and Jay Verkuilen. Fuzzy Set Theory. SAGE Publications, Inc., 2006. http://dx.doi.org/10.4135/9781412984300.

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Kandasamy, W. B. Vasantha. Set linear algebra and set fuzzy linear algebra. InfoLearnQuest, 2008.

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Som, Tanmoy, Oscar Castillo, Anoop Kumar Tiwari, and Shivam Shreevastava, eds. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8566-9.

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Zimmermann, H. J. Fuzzy set theory--and its applications. 2nd ed. Kluwer Academic Publishers, 1991.

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Cornelis, Chris, Glad Deschrijver, Mike Nachtegael, Steven Schockaert, and Yun Shi, eds. 35 Years of Fuzzy Set Theory. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16629-7.

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Zimmermann, H. J. Fuzzy Set Theory—and Its Applications. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8702-0.

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Zimmermann, H. J. Fuzzy Set Theory — and Its Applications. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0.

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Zimmermann, H. J. Fuzzy Set Theory—and Its Applications. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0646-0.

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Book chapters on the topic "Fuzzy Set. Empty Fuzzy Set"

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Novák, Vilém. "Fuzzy Set." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_5008-2.

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Novák, Vilém. "Fuzzy Set." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_5008.

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Weik, Martin H. "fuzzy set." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_7847.

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Novák, Vilém. "Fuzzy Set." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_5008.

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Kahlert, Jörg, and Hubert Frank. "Fuzzy-Set-Theorie." In Fuzzy-Logik und Fuzzy-Control. Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6_1.

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Kahlert, Jörg, and Hubert Frank. "Fuzzy-Set-Theorie." In Fuzzy-Logik und Fuzzy-Control. Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-83760-8_1.

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

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Lowen, R. "Fuzzy Logic." In Fuzzy Set Theory. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8741-9_6.

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Gottwald, Siegfried. "Basic fuzzy set theory." In Fuzzy Sets and Fuzzy Logic. Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-86812-1_2.

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Klement, Erich Peter, Radko Mesiar, and Endre Pap. "Fuzzy set theory." In Trends in Logic. Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9540-7_12.

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Conference papers on the topic "Fuzzy Set. Empty Fuzzy Set"

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Yao, Bing-xue, Jin-liang Liu, and Rui-xia Yan. "Fuzzy Soft Set and Soft Fuzzy Set." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.137.

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Jiang, Yi, Hailiang Zhao, and Junxuan He. "The relation between fuzzy soft set and integrative fuzzy set." In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2017. http://dx.doi.org/10.1109/iske.2017.8258761.

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Fu Li and Wang Pingshu. "Comparison of the concepts of soft set, fuzzy soft set and L-fuzzy set." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019607.

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Xu, Yong-jie, Yong-kan Sun, and Deng-feng Li. "Intuitionistic Fuzzy Soft Set." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473444.

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Pang, Yanjun, and Wei Pan. "The Normal Fuzzy Set." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.480.

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Patrascu, Vasile. "Penta-Valued Fuzzy Set." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295354.

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Jin-Cherng Lin and Kuo-Chiang Wu. "Fuzzy Decision Based on Fuzzy Rough Set." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253529.

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Maowen Nie and Woei Wan Tan. "Modeling capability of type-1 fuzzy set and interval type-2 fuzzy set." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251178.

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Yingjie Yang and R. John. "Roughness Bounds in Set-oriented Rough Set Operations." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681741.

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Kumar, Deepak, and S. B. Singh. "Evaluating fuzzy reliability using rough intuitionistic fuzzy set." In 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). IEEE, 2014. http://dx.doi.org/10.1109/cipech.2014.7019037.

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Reports on the topic "Fuzzy Set. Empty Fuzzy Set"

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Gluckman, Albert G., and Aivars Celmins. Cost Effectiveness Analysis Using Fuzzy Set Theory. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274003.

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Gavel, D. T. Fuzzy set classifier for waste classification tracking. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10129560.

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Goodman, I. R. Not All Fuzzy Set Operations Have Weak Homomorphic Random Set Counterparts. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240878.

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Esteva, Francesc. On Negations and Algebras in Fuzzy Set Theory. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada604012.

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Marques, Paulo. Outsiderness among young people in Europe: A fuzzy set analysis. DINÂMIA'CET-IUL, 2014. http://dx.doi.org/10.7749/dinamiacet-iul.wp.2014.03.

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Knapp, Benjamin, Mitra Dastmalchi, Eric Jacobs, and Shahab Layeghi. The Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada279148.

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Goodman, I. R., and V. M. Bier. A Re-Examination of the Relationship between Fuzzy Set Theory and Probability Theory. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240243.

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

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Seale, Maria, R. Salter, Natàlia Garcia-Reyero,, and Alicia Ruvinsky. A fuzzy epigenetic model for representing degradation in engineered systems. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45582.

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Degradation processes are implicated in a large number of system failures, and are crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representi
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Huang, Z., J. Shimeld, and M. Williamson. Application of computer neural network, and fuzzy set logic to petroleum geology, offshore eastern Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/194121.

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