Relevant bibliographies by topics / Relation of fuzzy preferrence

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  2. Dissertations / Theses
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Academic literature on the topic 'Relation of fuzzy preferrence'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Relation of fuzzy preferrence"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Relation of fuzzy preferrence"

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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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Ahmed, Uzair, and Muhammad Saqib. "Optimal Solutions Of Fuzzy Relation Equations." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3158.

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Fuzzy relation equations are becoming extremely important in order to investigate the optimal solution of the inverse problem even though there is a restrictive condition for the availability of the solution of such inverse problems. We discussed the methods for finding the optimal (maximum and minimum) solution of inverse problem of fuzzy relation equation of the form $R \circ Q = T$ where for both cases R and Q are kept unknown interchangeably using different operators (e.g. alpha, sigma etc.). The aim of this study is to make an in-depth finding of best project among the host of projects, d
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Williams, Larry Ritchie Jr. "The Use of Relation Valued Attributes in Support of Fuzzy Data." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3240.

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In his paper introducing fuzzy sets, L.A. Zadeh describes the difficulty of assigning some real-world objects to a particular class when the notion of class membership is ambiguous. If exact classification is not obvious, most people approximate using intuition and may reach agreement by placing an object in more than one class. Numbers or ‘degrees of membership’ within these classes are used to provide an approximation that supports this intuitive process. This results in a ‘fuzzy set’. This fuzzy set consists any number of ordered pairs to represent both the class and the class’s degree of
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Benkaddour, Saïd. "Relation entre ensembles totalement flous et ensembles ordonnés." Lyon 1, 1986. http://www.theses.fr/1986LYO11714.

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Le present travail fait suite aux recherches menees sur la theorie des ensembles flous. D. Ponasse a defini la categorie jtf des j-ensembles totalement flous. Dans sa these de 3eme cycle g. Mycek a demontre que cette categorie est un topos lorsque j est un anti-ordinal (c. A. D. : j est un ordinal lorsqu'il est muni de l'ordre inverse). Il a exhibe tous les objets elementaires de ce topos. Wu tao, lui, a fait une etude detaillee de ce topos avec j antiordinal. Dans leurs articles j. Coulon et j. L. Coulon montrent que pour j un treillis de heyting complet la categorie jtf est equivalente a la
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Jaffal, Hussein, and Cheng Tao. "Multiple Attributes Group Decision Making by Type-2 Fuzzy Sets and Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2659.

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We are living in a world full of uncertainty and ambiguity. We usually ask ourselves questions that we are uncertain about their answers. Is it going to rain tomorrow? What will be the exchange rate of euro next month? Why, where and how should I invest? Type-1 Fuzzy sets are characterized by the membership function whose value for a given element x is said to be the grade of membership having a value in the interval [0, 1]. In addition, type-1 fuzzy sets have limited capabilities to deal with uncertainty. In our thesis, we study another concept of a fuzzy description of uncertainty which is c
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Garcia, Ian. "Eliminating Redundant and Less-informative RSS News Articles Based on Word Similarity and A Fuzzy Equivalence Relation." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1688.pdf.

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Pierrard, Régis. "Explainable Classification and Annotation through Relation Learning and Reasoning." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST008.

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Avec les succés récents de l’apprentissage profond et les interactions toujours plus nombreuses entre êtres humains et intelligences artificielles, l’explicabilité est devenue une préoccupation majeure. En effet, il est difficile de comprendre le comportement des réseaux de neurones profonds, ce qui les rend inadaptés à une utilisation dans les systèmes critiques. Dans cette thèse, nous proposons une approche visant à classifier ou annoter des signaux tout en expliquant les résultats obtenus. Elle est basée sur l’utilisation d’un modèle transparent, dont le raisonnement est clair, et de relati
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Karanfil, Salih. "Obtaining the membership function by using the neural network in Istanbul stock exchange to find the relation between the low and closing prices." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/96007.

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Hirsch, Gérard. "Équations de relation floue et mesures d'incertain en reconnaissance de formes." Nancy 1, 1987. http://www.theses.fr/1987NAN10030.

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Il est appelé que le sylogisme indirect n'est pas parfait quelque soit l'opérateur de composition floue. Un opérateur de maximalisation (ou de minimalisation) est déterminé pour la composition sup-T norme (ou INF-T conorme). Après la reprise des résultats des mesures d'incertain il est donné une application numérique au problème de classification des phonèmes
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Wagner, Nicolas. "Détection des modifications de l’organisation circadienne des activités des animaux en relation avec des états pré-pathologiques, un stress, ou un événement de reproduction." Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAC032.

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L'élevage de précision consiste à enregistrer des paramètres sur les animaux ou leur environnement grâce à divers capteurs. Dans cette thèse, il s'agit de suivre le comportement de vaches laitières via un système de localisation en temps réel. Les données sont collectées en une suite de valeurs à intervalle régulier, c'est ce que l'on appelle une série temporelle. Les problèmes liés à l'utilisation de capteurs sont le grand nombre de données engendré et la qualité de ces données. Le Machine Learning (ML) permet d'atténuer ce problème. Le but de cette thèse est de détecter les comportements ano
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Books on the topic "Relation of fuzzy preferrence"

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Nola, Antonio. Fuzzy Relation Equations and Their Applications to Knowledge Engineering. Springer Netherlands, 1989.

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di Nola, Antonio, Salvatore Sessa, Witold Pedrycz, and Elie Sanchez. Fuzzy Relation Equations and Their Applications to Knowledge Engineering. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-017-1650-5.

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Kreinovich, Vladik. Fast parallel algorithms that compute transitive closure of a fuzzy relation. National Aeronautics and Space Administration, 1993.

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Jiménez-Losada, Andrés. Models for Cooperative Games with Fuzzy Relations among the Agents: Fuzzy Communication, Proximity Relation and Fuzzy Permission. Springer, 2018.

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Antonio, Di Nola, ed. Fuzzy relation equations and their applications to knowledge engineering. Kluwer Academic, 1989.

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Pedrycz, Witold, Antonio Di Nola, and S. Sessa. Fuzzy Relation Equations and Their Applications to Knowledge Engineering. Springer, 2014.

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B, Sheridan Thomas, and Lyndon B. Johnson Space Center. Information Technology Division., eds. Expert system training and control based on the fuzzy relation matrix: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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Book chapters on the topic "Relation of fuzzy preferrence"

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Novák, Vilém. "Fuzzy Relation." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_5010.

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Novák, Vilém. "Fuzzy Relation." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_5010.

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Cao, Bing-Yuan, Ji-Hui Yang, Xue-Gang Zhou, Zeinab Kheiri, Faezeh Zahmatkesh, and Xiao-Peng Yang. "Fuzzy Relation." In Fuzzy Relational Mathematical Programming. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33786-5_2.

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Novák, Vilém. "Fuzzy Relation." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_5010-2.

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Zhang, Yu-hong, and Xue-hai Yuan. "Soft Relation and Fuzzy Soft Relation." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38667-1_21.

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Lin, Hao-Ran, Bing-Yuan Cao, and Yun-zhang Liao. "Matrix, Fuzzy Relation and Fuzzy Matrix." In Fuzzy Sets Theory Preliminary. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70749-5_2.

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Kalina, Martin. "Fuzzy Limits and Fuzzy Nearness Relation." In Computational Intelligence. Theory and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_74.

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Cao, Nhung, and Martin Štěpnička. "Fuzzy Relation Equations with Fuzzy Quantifiers." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66830-7_32.

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Cao, Bing-Yuan, Ji-Hui Yang, Xue-Gang Zhou, Zeinab Kheiri, Faezeh Zahmatkesh, and Xiao-Peng Yang. "Fuzzy Relation Geometric Programming." In Fuzzy Relational Mathematical Programming. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33786-5_5.

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Zhang, Yu-hong, Xue-hai Yuan, and Zun-quan Xia. "On Fuzzy Soft Relation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30874-6_12.

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Conference papers on the topic "Relation of fuzzy preferrence"

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Yang, Jihui. "Fuzzy relation linear programming." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255037.

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Zhou Wei and Bao Menghong. "Intuitionistic fuzzy relation equations." In 2010 International Conference on Educational and Information Technology (ICEIT). IEEE, 2010. http://dx.doi.org/10.1109/iceit.2010.5607565.

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ZHANG, MINGYI, and YING ZHANG. "FUZZY SEMI-EQUIVALENT RELATION." In Proceedings of the 8th International FLINS Conference. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812799470_0025.

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Qinghua Hu, Daren Yu, and Congxin Wu. "Fuzzy preference relation rough sets." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664721.

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Bara, A. "Algorithm for Fuzzy Relation Identification." In 2006 IEEE International Conference on Automation, Quality and Testing, Robotics. IEEE, 2006. http://dx.doi.org/10.1109/aqtr.2006.254505.

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Nozari, T., and N. Fahimi. "Fundamental relation in fuzzy hyperrings." In 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE, 2015. http://dx.doi.org/10.1109/cfis.2015.7391668.

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NOVÁK, VILÉM, IRINA PERFILIEVA, and SIEGFRIED GOTTWALD. "FUZZY RELATION EQUATIONS VIA BASIC PREDICATE FUZZY LOGIC." In Proceedings of the 5th International FLINS Conference. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777102_0011.

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Arneja, I. S., and B. Venkatesh. "Probabilistic OPF using linear fuzzy relation." In 2012 10th International Power & Energy Conference (IPEC). IEEE, 2012. http://dx.doi.org/10.1109/asscc.2012.6523336.

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Mitra, Nirmal Kanti, and Maheswara Rao Valluri. "Maximum solution of fuzzy relation equation." In 2014 Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2014. http://dx.doi.org/10.1109/apwccse.2014.7053879.

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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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