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Journal articles on the topic 'Relation of fuzzy preferrence'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Šešelja, Branimir. "-fuzzy covering relation." Fuzzy Sets and Systems 158, no. 22 (2007): 2456–65. http://dx.doi.org/10.1016/j.fss.2007.05.019.

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2

Khosravi Shoar, S., R. A. Borzooei, and R. Moradian. "Fuzzy congruence relation generated by a fuzzy relation in vector spaces." Journal of Intelligent & Fuzzy Systems 35, no. 5 (2018): 5635–45. http://dx.doi.org/10.3233/jifs-17088.

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3

Rakityanskaya, A. B., and A. P. Rotshtein. "Fuzzy relation-based diagnosis." Automation and Remote Control 68, no. 12 (2007): 2198–213. http://dx.doi.org/10.1134/s0005117907120089.

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4

Gupta, K. C., and R. K. Gupta. "Fuzzy equivalence relation redefined." Fuzzy Sets and Systems 79, no. 2 (1996): 227–33. http://dx.doi.org/10.1016/0165-0114(95)00155-7.

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5

Kundu, Sukhamay. "Preference relation on fuzzy utilities based on fuzzy leftness relation on intervals." Fuzzy Sets and Systems 97, no. 2 (1998): 183–91. http://dx.doi.org/10.1016/s0165-0114(96)00350-8.

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6

Dutta, Soma, and Mihir K. Chakraborty. "Fuzzy relation and fuzzy function over fuzzy sets: a retrospective." Soft Computing 19, no. 1 (2014): 99–112. http://dx.doi.org/10.1007/s00500-014-1356-z.

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7

DI NOLA, ANTONIO, WITOLD PEDRYCZ, and SALVATORE SESSA. "PROCESSING OF FUZZY NUMBERS BY FUZZY RELATION EQUATIONS." Kybernetes 15, no. 1 (1986): 43–47. http://dx.doi.org/10.1108/eb005730.

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8

Das, M., M. K. Chakraborty, and T. K. Ghoshal. "Fuzzy tolerance relation, fuzzy tolerance space and basis." Fuzzy Sets and Systems 97, no. 3 (1998): 361–69. http://dx.doi.org/10.1016/s0165-0114(97)00253-4.

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9

Tsaur, Ruey-Chyn, Jia-Chi O Yang, and Hsiao-Fan Wang. "Fuzzy relation analysis in fuzzy time series model." Computers & Mathematics with Applications 49, no. 4 (2005): 539–48. http://dx.doi.org/10.1016/j.camwa.2004.07.014.

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10

Vijay, V., A. Mehra, S. Chandra, and C. R. Bector. "Fuzzy matrix games via a fuzzy relation approach." Fuzzy Optimization and Decision Making 6, no. 4 (2007): 299–314. http://dx.doi.org/10.1007/s10700-007-9015-9.

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11

Ćirić, Miroslav, Aleksandar Stamenković, Jelena Ignjatović, and Tatjana Petković. "Fuzzy relation equations and reduction of fuzzy automata." Journal of Computer and System Sciences 76, no. 7 (2010): 609–33. http://dx.doi.org/10.1016/j.jcss.2009.10.015.

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12

LLAMAZARES, BONIFACIO, and BERNARD DE BAETS. "FUZZY STRICT PREFERENCE RELATIONS COMPATIBLE WITH FUZZY ORDERINGS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 01 (2010): 13–24. http://dx.doi.org/10.1142/s0218488510006350.

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One of the most important issues in the field of fuzzy preference modelling is the construction of a fuzzy strict preference relation and a fuzzy indifference relation from a fuzzy weak preference relation. Here, we focus on a particular class of fuzzy weak preference relations, the so-called fuzzy orderings. The definition of a fuzzy ordering involves a fuzzy equivalence relation and, in this paper, the latter will be considered as the corresponding fuzzy indifference relation. We search for fuzzy strict preference relations compatible with a given fuzzy ordering and its fuzzy indifference re

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13

HUNG, WEN-LIANG, JINN-SHING LEE, and CHENG-DER FUH. "FUZZY CLUSTERING BASED ON INTUITIONISTIC FUZZY RELATIONS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 04 (2004): 513–29. http://dx.doi.org/10.1142/s0218488504002953.

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It is well known that an intuitionistic fuzzy relation is a generalization of a fuzzy relation. In fact there are situations where intuitionistic fuzzy relations are more appropriate. This paper discusses the fuzzy clustering based on intuitionistic fuzzy relations. On the basis of max -t & min -s compositions, we discuss an n-step procedure which is an extension of Yang and Shih's [17] n-step procedure. A similarity-relation matrix is obtained by beginning with a proximity-relation matrix using the proposed n-step procedure. Then we propose a clustering algorithm for the similarity-relati

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14

Nozari, Tahereh, and Najmeh Fahimi. "Fuzzy hyperrings and fundamental relation." Journal of Intelligent & Fuzzy Systems 30, no. 3 (2016): 1311–17. http://dx.doi.org/10.3233/ifs-152045.

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15

李, 季. "Block Composition of Fuzzy Relation." Hans Journal of Data Mining 08, no. 03 (2018): 142–50. http://dx.doi.org/10.12677/hjdm.2018.83016.

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16

Alkouri, Abd Ulazeez M., and Abdul Razak Salleh. "Complex Atanassov's Intuitionistic Fuzzy Relation." Abstract and Applied Analysis 2013 (2013): 1–18. http://dx.doi.org/10.1155/2013/287382.

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This paper presents distance measure between two complex Atanassov's intuitionistic fuzzy sets (CAIFSs). This distance measure is used to illustrate an application of CAIFSs in solving one of the most core application areas of fuzzy set theory, which is multiattributes decision-making (MADM) problems, in complex Atanassov's intuitionistic fuzzy realm. A new structure of relation between two CAIFSs, called complex Atanassov's intuitionistic fuzzy relation (CAIFR), is obtained. This relation is formally generalised from a conventional Atanassov's intuitionistic fuzzy relation, based on complex A

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17

Sahu, Mamata, and Anjana Gupta. "Incomplete hesitant fuzzy preference relation." Journal of Statistics and Management Systems 21, no. 8 (2018): 1459–79. http://dx.doi.org/10.1080/09720510.2018.1498188.

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18

Lee, S. H., and D. Zhang. "Dual fuzzy similarity relation equations." Computers & Mathematics with Applications 27, no. 11 (1994): 49–53. http://dx.doi.org/10.1016/0898-1221(94)90097-3.

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19

Davvaz, B., and N. Firouzkouhi. "Fundamental relation on fuzzy hypermodules." Soft Computing 23, no. 24 (2019): 13025–33. http://dx.doi.org/10.1007/s00500-019-04299-3.

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20

Bustince, H., E. Barrenechea, J. Fernandez, M. Pagola, J. Montero, and C. Guerra. "Contrast of a fuzzy relation." Information Sciences 180, no. 8 (2010): 1326–44. http://dx.doi.org/10.1016/j.ins.2009.12.013.

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21

Ignjatović, Jelena, Miroslav Ćirić, and Vesna Simović. "Fuzzy relation equations and subsystems of fuzzy transition systems." Knowledge-Based Systems 38 (January 2013): 48–61. http://dx.doi.org/10.1016/j.knosys.2012.02.008.

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22

Sarna, Marian. "Fuzzy relation on fuzzy and non-fuzzy numbers — fast computational formulas: II." Fuzzy Sets and Systems 93, no. 1 (1998): 63–74. http://dx.doi.org/10.1016/s0165-0114(96)00201-1.

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23

Liu, Xiao Jing, Wei Feng Du, and Xiao Min. "Fuzzy Attribute Reduction Based on Fuzzy Similarity." Applied Mechanics and Materials 533 (February 2014): 237–41. http://dx.doi.org/10.4028/www.scientific.net/amm.533.237.

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The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the pr

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24

Zhai, Yuling, Zeshui Xu, and Huchang Liao. "The Stably Multiplicative Consistency of Fuzzy Preference Relation and Interval-Valued Hesitant Fuzzy Preference Relation." IEEE Access 7 (2019): 54929–45. http://dx.doi.org/10.1109/access.2019.2910123.

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25

Kologani, Mona Aaly, Mohammad Mohseni Takallo, and Hee Sik Kim. "Fuzzy Filters of Hoops Based on Fuzzy Points." Mathematics 7, no. 5 (2019): 430. http://dx.doi.org/10.3390/math7050430.

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In this paper, we define the concepts of ( ∈ , ∈ ) and ( ∈ , ∈ ∨ q ) -fuzzy filters of hoops, discuss some properties, and find some equivalent definitions of them. We define a congruence relation on hoops by an ( ∈ , ∈ ) -fuzzy filter and show that the quotient structure of this relation is a hoop.

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26

Nesimovic, Sanela, and Dzenan Gusic. "Willmott Fuzzy Implication in Fuzzy Databases." WSEAS TRANSACTIONS ON MATHEMATICS 19 (January 19, 2021): 647–61. http://dx.doi.org/10.37394/23206.2020.19.72.

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The object of the research are fuzzy functional dependencies on given relation scheme, and the question of their obtaining using the classical and innovated techniques. The attributes of the universal set are associated to the elements of the unit interval, and are turned into fuzzy formulas in this way. We prove that the dependency (which is treated as a fuzzy formula with respect to appropriately chosen valuation) is valid whenever it agrees with the attached two-elements fuzzy relation instance. The opposite direction of the claim is proven to be incorrect in this setting. Generalizing thin

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27

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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28

Barkat, Omar, Lemnaouar Zedam, and Bernard De Baets. "On the Compatibility of a Ternary Relation with a Binary Fuzzy Relation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 04 (2019): 595–612. http://dx.doi.org/10.1142/s0218488519500260.

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Recently, De Baets et al. have characterized the fuzzy tolerance relations that a given strict order relation is compatible with. In general, the compatibility of a strict order relation with a binary fuzzy relation guarantees also the compatibility of its associated betweenness relation with that binary fuzzy relation. In this paper, we study the compatibility of an arbitrary ternary relation with a binary fuzzy relation. We prove that this compatibility can be expressed in terms of inclusions of the binary fuzzy relation in the traces of the given ternary relation.

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29

ELORZA, J., and P. BURILLO. "ON THE RELATION BETWEEN FUZZY PREORDERS AND FUZZY CONSEQUENCE OPERATORS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 03 (1999): 219–34. http://dx.doi.org/10.1142/s0218488599000167.

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The purpose of this paper is to analyze the operators induced by relations and conversely the relations induced by operators in fuzzy logic. Given a t-norm * and given a non-empty universal set X, it is well known that if R is a fuzzy *-preorder on X then the operator induced by R, [Formula: see text], is a fuzzy consequence operator (FCO). In fact, [Formula: see text] is a *-coherent FCO. It is also known that if C is a *-coherent FCO then the relation induced by C, RC, is a fuzzy *-preorder. We explore the *-coherence axiom because we do not know in the literature any example of non-coherent

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30

Di Nola, Antonio, Witold Pedrycz, and Salvatore Sessa. "When is a fuzzy relation decomposable in two fuzzy sets." Fuzzy Sets and Systems 16, no. 1 (1985): 87–90. http://dx.doi.org/10.1016/s0165-0114(85)80008-7.

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31

KOŁODZIEJCZYK, WALDEMAR. "ON TRANSITIVE SOLUTIONS OF $-FUZZY RELATION EQUATIONS DESCRIBING FUZZY SYSTEMS." International Journal of General Systems 17, no. 2-3 (1990): 277–88. http://dx.doi.org/10.1080/03081079008935111.

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32

Stamou, G. B., and S. G. Tzafestas. "Fuzzy relation equations and fuzzy inference systems: an inside approach." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 29, no. 6 (1999): 694–702. http://dx.doi.org/10.1109/3477.809025.

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33

ZHANG, Ling, and Lun-Wen WANG. "Best Approximation for Fuzzy Tolerance Relation." Chinese Journal of Computers 36, no. 11 (2014): 2274–82. http://dx.doi.org/10.3724/sp.j.1016.2013.02274.

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34

LU, Yan-li, Ying-jie LEI, and Zhao-yuan LI. "Construction of intuitionistic fuzzy similarity relation." Journal of Computer Applications 28, no. 2 (2008): 311–14. http://dx.doi.org/10.3724/sp.j.1087.2008.00311.

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35

Das, S. K. "Inductive Learning of Complex Fuzzy Relation." International Journal of Computer Science, Engineering and Information Technology 1, no. 5 (2011): 29–38. http://dx.doi.org/10.5121/ijcseit.2011.1503.

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36

Mei, Jian-Ping, and Lihui Chen. "LinkFCM: Relation integrated fuzzy c-means." Pattern Recognition 46, no. 1 (2013): 272–83. http://dx.doi.org/10.1016/j.patcog.2012.06.012.

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37

Di Martino, Ferdinando, and Salvatore Sessa. "Spatial Analysis and Fuzzy Relation Equations." Advances in Fuzzy Systems 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/429498.

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We implement an algorithm that uses a system of fuzzy relation equations (SFRE) with the max-min composition for solving a problem of spatial analysis. We integrate this algorithm in a Geographical Information System (GIS) tool, and the geographical area under study is divided in homogeneous subzones (with respect to the parameters involved) to which we apply our process to determine the symptoms after that an expert sets the SFRE with the values of the impact coefficients. We find that the best solutions and the related results are associated to each subzone. Among others, we define an index

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38

Hu, Cheng-Feng. "Generalized variational inequalities with fuzzy relation." Journal of Computational and Applied Mathematics 146, no. 1 (2002): 47–56. http://dx.doi.org/10.1016/s0377-0427(02)00417-x.

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39

Guangzhi Li and Shu-Cherng Fang. "Solving interval-valued fuzzy relation equations." IEEE Transactions on Fuzzy Systems 6, no. 2 (1998): 321–24. http://dx.doi.org/10.1109/91.669033.

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40

WANG, HSIAO-FAN, and HSI-MEI HSU. "SENSITIVITY ANALYSIS OF FUZZY RELATION EQUATIONS." International Journal of General Systems 19, no. 2 (1991): 155–69. http://dx.doi.org/10.1080/03081079108935169.

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41

Cuong, Bui Cong, Nguyen Hoang Phuong, Phan Hoang Anh, and Koichi Yamada. "Fuzzy Relation with Thresholds and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 6, no. 1 (2002): 2–6. http://dx.doi.org/10.20965/jaciii.2002.p0002.

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This paper concerns fuzzy relations with thresholds and some applications. Firstly, we consider fuzzy relations with thresholds-relation arising from many problems of real-world life, then the extension of some fuzzy inference methods using t-norms with thresholds are discussed and some calculations in SQL query forms are considered.

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42

Jacob, Rogi, and Sunny Kuriakose A. "Near sets through fuzzy similarity relation." Applied Mathematical Sciences 8 (2014): 2035–40. http://dx.doi.org/10.12988/ams.2014.42104.

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43

Di Nola, Antonio, Salvatore Sessa, and Witold Pedrycz. "On some finite fuzzy relation equations." Information Sciences 50, no. 1 (1990): 93–109. http://dx.doi.org/10.1016/0020-0255(90)90006-v.

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44

Le, Kim. "Fuzzy relation compositions and pattern recognition." Information Sciences 89, no. 1-2 (1996): 107–30. http://dx.doi.org/10.1016/0020-0255(95)00231-6.

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45

Dubois, Didier, and Henri Prade. "Fuzzy relation equations and causal reasoning." Fuzzy Sets and Systems 75, no. 2 (1995): 119–34. http://dx.doi.org/10.1016/0165-0114(95)00105-t.

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46

Liang, Gin-Shuh, and Mao-Jiun J. Wang. "Evaluating human reliability using fuzzy relation." Microelectronics Reliability 33, no. 1 (1993): 63–80. http://dx.doi.org/10.1016/0026-2714(93)90046-2.

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47

Abbasi Molai, Ali. "Fuzzy linear objective function optimization with fuzzy-valued max-product fuzzy relation inequality constraints." Mathematical and Computer Modelling 51, no. 9-10 (2010): 1240–50. http://dx.doi.org/10.1016/j.mcm.2010.01.006.

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48

Lee, Jeong-Gon, and Kul Hur. "Bipolar Fuzzy Relations." Mathematics 7, no. 11 (2019): 1044. http://dx.doi.org/10.3390/math7111044.

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We introduce the concepts of a bipolar fuzzy reflexive, symmetric, and transitive relation. We study bipolar fuzzy analogues of many results concerning relationships between ordinary reflexive, symmetric, and transitive relations. Next, we define the concepts of a bipolar fuzzy equivalence class and a bipolar fuzzy partition, and we prove that the set of all bipolar fuzzy equivalence classes is a bipolar fuzzy partition and that the bipolar fuzzy equivalence relation is induced by a bipolar fuzzy partition. Finally, we define an ( a , b ) -level set of a bipolar fuzzy relation and investigate

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49

Abu- Gdairi, Radwan, and Ibrahim Noaman. "Generating Fuzzy Sets and Fuzzy Relations Based on Information." WSEAS TRANSACTIONS ON MATHEMATICS 20 (April 21, 2021): 178–85. http://dx.doi.org/10.37394/23206.2021.20.19.

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Fuzzy set theory and fuzzy relation are important techniques in knowledge discovery in databases. In this work, we presented fuzzy sets and fuzzy relations according to some giving Information by using rough membership function as a new way to get fuzzy set and fuzzy relation to help the decision in any topic . Some properties have been studied. And application of my life on the fuzzy set was introduced

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50

Gheorghe, Razvan, Ahmed Bufardi, and Paul Xirouchakis. "Construction of a two-parameters fuzzy outranking relation from fuzzy evaluations." Fuzzy Sets and Systems 143, no. 3 (2004): 391–412. http://dx.doi.org/10.1016/s0165-0114(03)00227-6.

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