Relevant bibliographies by topics / Fuzzy relation model

Contents

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

Academic literature on the topic 'Fuzzy relation model'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Fuzzy relation model"

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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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Oh, Hyonil, and Jungchol Cho. "A Fractional Programming Model for Improving Multiplicative Consistency of Intuitionistic Fuzzy Preference Relations." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 05 (2022): 879–96. http://dx.doi.org/10.1142/s021848852250026x.

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In this paper, we propose a method that improves multiplicative consistency based on a fractional programming model to derive the normalized intuitionistic fuzzy priority weight vector from an intuitionistic fuzzy preference relation. To do so, a new definition is formulated that captures previous definitions for multiplicative consistency of intuitionistic fuzzy preference relations. A transformation formula is proposed to convert the normalized intuitionistic fuzzy priority weight vector into a multiplicative consistent intuitionistic fuzzy preference relation. By using the properties of som
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INTAN, ROLLY, and MASAO MUKAIDONO. "GENERALIZED FUZZY ROUGH SETS BY CONDITIONAL PROBABILITY RELATIONS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (2002): 865–81. http://dx.doi.org/10.1142/s0218001402002039.

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In 1982, Pawlak proposed the concept of rough sets with a practical purpose of representing indiscernibility of elements or objects in the presence of information systems. Even if it is easy to analyze, the rough set theory built on a partition induced by equivalence relation may not provide a realistic view of relationships between elements in real-world applications. Here, coverings of, or nonequivalence relations on, the universe can be considered to represent a more realistic model instead of a partition in which a generalized model of rough sets was proposed. In this paper, first a weak f
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Liu, Yuan, Gongtian Shen, Zhangyan Zhao, and Zhanwen Wu. "A New Model for Deriving the Priority Weights from Hesitant Triangular Fuzzy Preference Relations." Mathematical Problems in Engineering 2019 (February 4, 2019): 1–12. http://dx.doi.org/10.1155/2019/8586592.

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Fuzzy preference relation is a common tool to express the uncertain preference information of decision maker in the process of decision making. However, the traditional fuzzy preference relation will fail under hesitant fuzzy environment as the membership has a single value. In addition, it is very difficult to obtain the precise membership values. Therefore, a new model of fuzzy preference relation is proposed in this paper. Firstly, the concept of hesitant triangular fuzzy preference relation is defined and its properties are investigated based on the concepts of hesitant fuzzy set, hesitant
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Ikoma, Norikazu, and Kaoru Hirota. "Nonlinear autoregressive model based on fuzzy relation." Information Sciences 71, no. 1-2 (1993): 131–44. http://dx.doi.org/10.1016/0020-0255(93)90068-w.

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Poleshchuk, O. M. "Cluster analysis of expert information based on Z-numbers." Forestry Bulletin 26, no. 1 (2022): 143–48. http://dx.doi.org/10.18698/2542-1468-2022-1-143-148.

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The paper developed a model of cluster analysis of expert criteria for assessing qualitative (non-numerical) characteristics with a certain level of reliability. To formalize individual criteria, Z-numbers are used, which are ordered pairs of ordinary fuzzy numbers. The first number is an estimate of the characteristic, and the second number is its reliability. For Z-numbers and their first components the aggregating indicators was defined based on a-cuts of fuzzy numbers. Aggregating indicators are used to determine the pairwise difference indexes of expert criteria and pairwise similarity in
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Yang, Xuan, and Zhou-Jing Wang. "Geometric Least Square Models for Deriving[0,1]-Valued Interval Weights from Interval Fuzzy Preference Relations Based on Multiplicative Transitivity." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/180892.

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This paper presents a geometric least square framework for deriving[0,1]-valued interval weights from interval fuzzy preference relations. By analyzing the relationship among[0,1]-valued interval weights, multiplicatively consistent interval judgments, and planes, a geometric least square model is developed to derive a normalized[0,1]-valued interval weight vector from an interval fuzzy preference relation. Based on the difference ratio between two interval fuzzy preference relations, a geometric average difference ratio between one interval fuzzy preference relation and the others is defined
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Bidin, Mohd Syafiq, Abd Fatah Wahab, Mohammad Izat Emir Zulkifly, and Rozaimi Zakaria. "Generalized Fuzzy Linguistic Bicubic B-Spline Surface Model for Uncertain Fuzzy Linguistic Data." Symmetry 14, no. 11 (2022): 2267. http://dx.doi.org/10.3390/sym14112267.

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A fuzzy linguistic data set that is uncertain is difficult to analyze and describe in the form of a smooth and continuous generic figure. Therefore, the study aims to develop a new model of a B-spline surface using a different approach of a crisp and fuzzy linguistic point relation with three types of linguistic function: low L, medium Mi and high H. These linguistic functions are defined first to introduce the fuzzy linguistic point relation. Then, a new algorithm of the fuzzy linguistic bicubic B-spline surface model is presented to convert fuzzy linguistic data into fuzzy linguistic control
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Koppula, Kavitha, Babushri Srinivas Kedukodi, and Syam Prasad Kuncham. "Generalization and ranking of fuzzy numbers by relative preference relation." Soft Computing 26, no. 3 (2021): 1101–22. http://dx.doi.org/10.1007/s00500-021-06616-1.

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AbstractWe define $$2n+1$$ 2 n + 1 and 2n fuzzy numbers, which generalize triangular and trapezoidal fuzzy numbers, respectively. Then, we extend the fuzzy preference relation and relative preference relation to rank $$2n+1$$ 2 n + 1 and 2n fuzzy numbers. When the data is representable in terms of $$2n+1$$ 2 n + 1 fuzzy number, we generalize the FMCDM (fuzzy multi-criteria decision making) model constructed with TOPSIS and relative preference relation. Lastly, we give an example from telecommunications to present the proposed FMCDM model and validate the results obtained.
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Shaheen, Tanzeela, Wajid Ali, Bilal Hussain, and Afshan Qayyum. "A NOVEL MULTI-GRANULATION MODEL BASED ON -FUZZIFIED ROUGH SET ENVIRONMENT AND ITS APPLICATION IN CLASSIFICATION." Advances in Fuzzy Sets and Systems 29, no. 1 (2024): 39–68. https://doi.org/10.17654/0973421x24003.

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Rough set (RS) and generalized rough set theories utilize single relations to obtain approximations of sets on a given universe of discourse. In granular computation, this is called single granularity. This article first expands -fuzzified RSs established on fuzzy tolerance relation to -optimistic multi-granulation fuzzified RSs by using a set of tolerance fuzzy relations over a given universe. Moreover, several elementary measures are proposed in this framework. Its application in feature selection has been highlighted through experimental analysis.
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Dissertations / Theses on the topic "Fuzzy relation model"

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Wang, Yanfei. "Fuzzy methods for analysis of microarrays and networks." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48175/1/Yanfei_Wang_Thesis.pdf.

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Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) c
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Imam, Ayad Tareq. "Relative-fuzzy : a novel approach for handling complex ambiguity for software engineering of data mining models." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/3909.

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There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed appro
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Sanghi, Shweta. "Membership Functions for a Fuzzy Relational Database: A Comparison of the Direct Rating and New Random Proportional Methods." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd/1366.

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Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum
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Arinsoy, Aslican. "Maximization of Delivery-Based Customer Satisfaction Considering Customer-Job Relationships in a Multi-Period Environment." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1378426684.

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Cheng, Mei-Fang, and 鄭美芳. "Linear Programming Model of an Inconsistent Multiplicative Intuitionistic Fuzzy Preference Relation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q66fr4.

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碩士<br>國立高雄應用科技大學<br>工業工程與管理系碩士在職專班<br>106<br>This paper considers the problem of the goal programming optimization model proposed by Gong et al.(2009) for an inconsistent multiplicative intuitionistic fuzzy preference relation. For the three alternatives, a reduced goal programming optimization model is proposed in this paper. Three special inconsistent multiplicative intuitionistic fuzzy preference relations are considered which make the optimal upper priority weights equal to the lower priority weights. For each special inconsistent multiplicative intuitionistic fuzzy preference relation, fo
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Shen, Chun-Han, and 沈均翰. "Application of Adaptive Network-Based Fuzzy Inference System model on Rainfall and Water Level Relation Predication for Yilan Shin-Nan Region." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/w2h3d4.

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碩士<br>國立宜蘭大學<br>土木工程學系碩士班<br>103<br>In Taiwan, the methods currently used for flooding area estimation are mainly based on the after-survey of the flood mark. These methods can not reflect the real-time situation of the flooding area, and hence can’t able to provide immediate information for damage control. In this study, we set up automatic water stage monitoring system at the Mei-Fu region where is selected as the target area of the project due to frequent flood history of this area. We set many model and used the ANFIS(Adaptive Network-Based Fuzzy Inference System) to training the rainfall
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Kang, Li-Chun, and 康麗君. "Similarity-Relationship Based Fuzzy Relational Database Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/7u5h52.

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碩士<br>中原大學<br>數學研究所<br>92<br>The system of fuzzy database is the theory that uses fuzzy mathematics to expand the traditional relation database. Its goal is to store imprecise information and to deal with inquiries of inexact information. In this dissertation, we introduce the essential concepts of the fuzzy set theory and relational database. Fuzzy theory was proposed by L. A. Zedah in1965. Some scholars did not apply it to database management system until 1980.To represent imprecise, uncertain, and incomplete information, we discuss the similarity-based fuzzy databases. This model extend
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Tsai, Shih-Feng, and 蔡世鋒. "Proximity-Relationship Based Fuzzy Relational Database Model." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/jc8q5h.

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碩士<br>中原大學<br>應用數學研究所<br>91<br>ABSTRACT The fuzzy relation database is the data mode which applies the fuzzy assemble and the fuzzy logic to the database system , and it is designed mainly to deal with these indefinite and incomplete data to become conformed to our requirement in use. The fuzzy relation database continued using the relation data mode of the traditional relation data mode , which it is mainly aimed at the inexact data with considering the possible distribution of the quality value degree. The kind of the fuzzy relation models are various, including the model of the fu
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Chen, Hui-Chun, and 陳惠君. "Complete Axioms of Fuzzy Multivalued Dependencies in a Fuzzy Relational Data Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/64186029745105598810.

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碩士<br>元智大學<br>資訊管理研究所<br>92<br>A fuzzy relational data model is an extension of the traditional relational data model that is mainly to process precise data. However, in the real world, there are a lot of uncertain and imprecise data. In order to deal with imprecise data, Zadeh introduced the theory of fuzzy sets based on the mathematical framework in 1965. In the fuzzy relational data model, different kinds of integrity constraints, such as fuzzy functional dependency, fuzzy multivalued dependency, fuzzy join dependency, etc., were added to relational database to filter and constrain its
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Chen, Jack, and 陳宗傑. "A Fuzzy Object-oriented Model for Relational Database." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/33365570059545070329.

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碩士<br>國立中央大學<br>資訊工程研究所<br>87<br>Object-oriented modeling has been widely utilized to model real-world concepts in various areas. However, real-world concepts are almost fuzzy in nature, and can't be model by traditional object oriented modeling approaches. Therefore, one of the foci of the recent development in object-oriented modeling has made progress toward the integration of object concepts with fuzzy logic. In this research, a new fuzzy object-oriented modeling approach, called FOOM, is proposed based fuzzy logic to analyze and capture informal requirements along several dimen
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Books on the topic "Fuzzy relation model"

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Jiménez-Losada, Andrés. Models for Cooperative Games with Fuzzy Relations among the Agents. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56472-2.

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Salanțiu, Tudor. Înțelegerea realității internaționale: Confruntarea adevărurilor istorice în procesele internaționale de evoluție. Presa Universitară Clujeană, 2021.

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Esipov, Yuriy, Besik Meshi, Mustafa Dzhilyadzhi, and Aleksandr Hazov. Scientific and applied tasks of technosphere safety. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1882552.

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The monograph sets and solves the following tasks in a complex: systematization of types of safety; formalization of the structure and models of accidents; unification of the concepts of "safety" and "risk", primarily in relation to technical systems "protection - object - subject - environment"; as well as the tasks of combining and (or) generalization of algorithms for calculating safety indicators and the risk of systems based on probabilistic and possibilistic (fuzzy) measures of occurrence of accidents and the establishment of application boundaries for "objective" and (or) "subjective" i
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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 International Publishing AG, 2017.

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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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Cao, Bing-Yuan, Ji-Hui Yang, Xue-Gang Zhou, Zeinab Kheiri, Faezeh Zahmatkesh, and Xiao-Peng Yang. Fuzzy Relational Mathematical Programming: Linear, Nonlinear and Geometric Programming Models. Springer, 2019.

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Dexter, Arthur L. Monitoring and Control of Information-Poor Systems: An Approach Based on Fuzzy Relational Models. Wiley & Sons, Incorporated, John, 2012.

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Dexter, Arthur L. Monitoring and Control of Information-Poor Systems: An Approach Based on Fuzzy Relational Models. Wiley & Sons, Limited, John, 2012.

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Dexter, Arthur L. Monitoring and Control of Information-Poor Systems: An Approach Based on Fuzzy Relational Models. Wiley & Sons, Incorporated, John, 2012.

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Dexter, Arthur L. Monitoring and Control of Information-Poor Systems: An Approach Based on Fuzzy Relational Models. Wiley & Sons, Incorporated, John, 2012.

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Book chapters on the topic "Fuzzy relation model"

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Xijing, Zhao, Wang Lixin, He Zhikun, and Li Rui. "Identification of FOG Multivariable Model Based on Fuzzy Relation." In Intelligence Computation and Evolutionary Computation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31656-2_54.

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Ren, Peijia, and Zeshui Xu. "A Priority Programming Model for Hesitant Fuzzy Linguistic Preference Relation." In Decision-Making Analyses with Thermodynamic Parameters and Hesitant Fuzzy Linguistic Preference Relations. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73253-0_6.

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Cao, Guojin, Jianxia Chen, Fan Yang, Chao Li, and Jie Zhang. "Research of Entity Relation Extraction Model Based on Dependency Parsing Neural Network." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32591-6_39.

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Yu, Dongxu, and Sidong Xian. "A Ranking Model for Intuitionistic Fuzzy Preference Relation Under Uncertainty for Targeted Poverty." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32456-8_77.

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Ren, Peijia, and Zeshui Xu. "A Consensus Model for Hesitant Fuzzy Linguistic Preference Relation Based on Consistency Driven." In Decision-Making Analyses with Thermodynamic Parameters and Hesitant Fuzzy Linguistic Preference Relations. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73253-0_8.

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Pȩkala, Barbara. "General Preference Structure with Uncertainty Data Present by Interval-Valued Fuzzy Relation and Used in Decision Making Model." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66827-7_14.

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Choi, Jeoung-Nae, Sung-Kwun Oh, and Hyun-Ki Kim. "Identification of Fuzzy Relation Model Using HFC-Based Parallel Genetic Algorithms and Information Data Granulation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_7.

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Chen, Guoqing. "The Relational Data Model." In Fuzzy Logic in Data Modeling. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4068-7_1.

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Jiménez-Losada, Andrés. "Fuzzy Communication." In Models for Cooperative Games with Fuzzy Relations among the Agents. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56472-2_4.

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Jiménez-Losada, Andrés. "Fuzzy Permission." In Models for Cooperative Games with Fuzzy Relations among the Agents. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56472-2_6.

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Conference papers on the topic "Fuzzy relation model"

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Yamasaki, Shingo, and Shingo Aoki. "Structured DEA model considering relation among input and output elements." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007536.

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Bosc, Patrick, Olivier Pivert, and Gregory Smits. "A database preference query model based on a fuzzy outranking relation." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584575.

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He, Jianhua, Yaolin Liu, and Yan Yu. "Fuzzy description model for indeterminate direction relation." In Geoinformatics 2007, edited by Peng Gong and Yongxue Liu. SPIE, 2007. http://dx.doi.org/10.1117/12.764603.

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Wei, Shu-Xiang, Ming-Hu Ha, and Yao-Feng Liu. "Sugeno rough set model under the fuzzy relation." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580553.

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Yu, Fajiang, Huanguo Zhang, and Fei Yan. "A Fuzzy Relation Trust Model in P2P System." In 2006 International Conference on Computational Intelligence and Security. IEEE, 2006. http://dx.doi.org/10.1109/iccias.2006.295309.

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Tang, Xuehua, and Kun Qin. "Direction-relation similarity model based on fuzzy close-degree." In 2010 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2010. http://dx.doi.org/10.1109/pic.2010.5687406.

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Jian, Li-rong, and Ming-yang Li. "An Extension of VPRS Model Based on Dominance Relation." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.159.

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Gao, Yuqing, Minghu Ha, Xia Zhao, and Chunjing Li. "Rough Set Model Based on Limited Similarity Dominance Relation." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.118.

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Fang, Meiyu, Xiaolin Zheng, and Deren Chen. "A Personalized Recommender Algorithm Based on Fuzzy Relation Reputation Model." In 2011 International Joint Conference on Service Sciences (IJCSS). IEEE, 2011. http://dx.doi.org/10.1109/ijcss.2011.45.

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Tang, Xinming, Wolfgang Kainz, and Hui Zhang. "Some Topological Invariants and a Qualitative Topological Relation Model between Fuzzy Regions." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.522.

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Reports on the topic "Fuzzy relation model"

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Kucherova, Hanna, Anastasiia Didenko, Olena Kravets, Yuliia Honcharenko, and Aleksandr Uchitel. Scenario forecasting information transparency of subjects' under uncertainty and development of the knowledge economy. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4469.

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Topicality of modeling information transparency is determined by the influence it has on the effectiveness of management decisions made by an economic entity in the context of uncertainty and information asymmetry. It has been found that information transparency is a poorly structured category which acts as a qualitative characteristic of information and at certain levels forms an additional spectrum of properties of the information that has been adequately perceived or processed. As a result of structuring knowledge about the factor environment, a fuzzy cognitive model of information transpar
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Blake, Carolyn, Benjamin P. Rigby, Roxanne Armstrong-Moore, et al. Participatory systems mapping for population health research, policy and practice: guidance on method choice and design. University of Glasgow, 2024. http://dx.doi.org/10.36399/gla.pubs.316563.

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What is participatory systems mapping? Participatory systems mapping engages stakeholders with varied knowledge and perspectives in creating a visual representation of a complex system. Its purpose is to explore, and document perceived causal relations between elements in the system. This guidance focuses on six causal systems mapping methods: systems-based theory of change maps; causal loop diagrams; CECAN participatory systems mapping; fuzzy cognitive maps; systems dynamics models; and Bayesian belief networks. What is the purpose of this guidance? This guidance includes a Framework that aid
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