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Books on the topic 'Fuzzy sets – Research'

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Published: 4 June 2021
Last updated: 9 February 2022

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1

Cao, Bing-Yuan, and Yu-Bin Zhong, eds. Fuzzy Sets and Operations Research. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02777-3.

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2

Dubois, Didier. Fundamentals of Fuzzy Sets. Boston, MA: Springer US, 2000.

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3

Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press, 2008.

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4

Tanaka, Hideo. Possibilistic data analysis for operations research. Heidelberg: Physica-Verlag, 1999.

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5

Słowiński, Roman. Fuzzy Sets in Decision Analysis, Operations Research and Statistics. Boston, MA: Springer US, 1998.

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6

Słowiński, Roman, ed. Fuzzy Sets in Decision Analysis, Operations Research and Statistics. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5645-9.

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7

Höhle, Ulrich. Mathematics of Fuzzy Sets: Logic, Topology, and Measure Theory. Boston, MA: Springer US, 1999.

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8

Fuzzy set theory and its applications. Boston: Kluwer-Nijhoff Pub., 1985.

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9

Fuzzy set theory--and its applications. 4th ed. Dordrecht: Kluwer Academic Publishers, 2001.

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10

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

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11

Zimmermann, H. J. Fuzzy set theory--and its applications. 3rd ed. Boston: Kluwer Academic Publishers, 1996.

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12

Zimmermann, H. J. Fuzzy sets and operations research for decision support: Decision support under uncertainity ; key selected papers by. Beijing: Beijing Normal University Press, 2000.

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13

International Symposium on Fuzzy Mathematics in Earthquake Researches (1985 Peking, China). Fuzzy mathematics in earthquake researches: Proceedings of International Symposium on Fuzzy Mathematics in Earthquake Researches. Beijing: Seismological Press, 1985.

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14

Schoppe, Anke. Behandlungsmöglichkeiten der Unschärfe von Daten und Relationen. Göttingen: Unitext, 1991.

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15

Lowen, R. Fuzzy Set Theory: Basic Concepts, Techniques and Bibliography. Dordrecht: Springer Netherlands, 1996.

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16

author, Huang Jih-Jeng, ed. Fuzzy multiple objective decision making. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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17

1929-, Hwang C. L., ed. Fuzzy mathematical programming: Methods and applications. Berlin: Springer-Verlag, 1992.

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18

Shu, Chang. Estimation régionale des débits de crues par la méthode ANFIS. Québec: INRS, Eau, terre et environnement, 2007.

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19

1929-, Hwang C. L., ed. Fuzzy multiple objective decision making: Methods and applications. Berlin: Springer-Verlag, 1994.

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20

Xiaoqiang, Cai, and SpringerLink (Online service), eds. Intuitionistic Fuzzy Information Aggregation: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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21

Bouchon-Meunier, Bernadette. Aggregation and Fusion of Imperfect Information. Heidelberg: Physica-Verlag HD, 1998.

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22

Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.

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Presents the current status in modelling of metallurgical processes considered by the model the mathematical model used in the description of the processes of copper production and their classification. Set out a system of methods and models in the field of mathematical modeling of technological processes, including balance sheet, statistics, optimization models, forecasting models and predictive models. For specific technological processes are developed: the model of the balance of the cycle of pyrometallurgical production of copper, polynomial model for prediction of matte composition on the basis of the passive experiment, predictive model of quantitative estimation of the copper content in the matte based on fuzzy logic. Of interest to students, postgraduates, teachers of technical universities, engineers and research workers who use mathematical methods for processing of data of laboratory and industrial experiments.
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23

Da, Ruan, ed. Fuzzy logic and intelligent technologies in nuclear science: Proceedings of the 1st International FLINS Workshop, Mol, Belgium, September 14-16, 1994. Singapore: World Scientific, 1994.

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24

Cao, Bing-Yuan, and Yu-Bin Zhong. Fuzzy Sets and Operations Research. Springer, 2019.

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25

Tamás, Zétényi, ed. Fuzzy sets in psychology. Amsterdam: North-Holland, 1988.

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26

Kandasamy, W. B. Vasantha, K. Ilanthenral, and Florentin Smarandache. Special Fuzzy Matrices for Social Scientists. InfoLearnQuest, 2007.

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27

Fuzzy-Set Social Science. University Of Chicago Press, 2000.

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28

Fuzzy-Set Social Science. University Of Chicago Press, 2000.

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29

Roman, Słowiński, ed. Fuzzy sets in decision analysis, operations research, and statistics. Boston: Kluwer Academic Publishers, 1998.

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30

Fuzzy Sets and Operations Research for Decision Support (Advances in Fuzzy Mathematics and Engineering). Beijing Normal University Press, 2000.

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31

Rihoux, Benoît. Case‐Oriented Configura‐Tional Research: Qualitative Comparative Analysis (Qca), Fuzzy Sets, and Related Techniques. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0031.

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This article investigates the tradition of case-oriented configurational research, focusing specifically on qualitative comparative analysis (QCA) as a tool for causal inference. It first presents two analytic procedures commonly used by comparative researchers. A short description of the state-of-the-art of QCA applications is offered, in terms of discipline, types of cases, models, combinations with other methods, and software development. It then reviews different uses of QCA, as well as generic ‘best practices’. Some key recent evolutions are illustrated: on the one hand the development, beyond dichotomous ‘crisp set’ QCA (csQCA), of multi-value QCA (mvQCA), fuzzy sets, and fuzzy-set QCA (fsQCA), and on the other hand technical advances and refinements in the use of the techniques. Finally, the article gives some concluding reflections as to expected developments, upcoming innovations, remaining challenges, expansion of fields of application, and cross-fertilization with other approaches.
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32

Bělohlávek, Radim, Joseph W. Dauben, and George J. Klir. Fuzzy Logic in the Broad Sense. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190200015.003.0003.

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The chapter begins by introducing the important and useful distinction between the research agendas of fuzzy logic in the narrow and the broad senses. The chapter deals with the latter agenda, whose ultimate goal is to employ intuitive fuzzy set theory for emulating commonsense human reasoning in natural language and other unique capabilities of human beings. Restricting to standard fuzzy sets, whose membership degrees are real numbers in the unit interval [0,1], the chapter describes how this broad agenda has become increasingly specific via the gradual development of standard fuzzy set theory and the associated fuzzy logic. An overview of currently recognized nonstandard fuzzy sets, which open various new directions in fuzzy logic, is presented in the last section of this chapter.
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33

Hwang, Ching-Lai, and Young-Jou Lai. Fuzzy Mathematical Programming: Methods and Applications. Springer, 2014.

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34

Mareš, Milan. Fuzzy cooperative games: Cooperation with vague expectations. 2001.

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35

Cai, Xiaoqiang, and Zeshui Xu. Intuitionistic Fuzzy Information Aggregation: Theory and Applications. Springer, 2013.

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36

Hwang, Ching-Lai, and Young-Jou Lai. Fuzzy Mathematical Programming: Methods and Applications (Lecture Notes in Economics and Mathematical Systems). Springer, 1995.

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37

Hwang, Ching-Lai, and Young-Jou Lai. Fuzzy Mathematical Programming: Methods and Applications (Lecture Notes in Economics and Mathematical Systems). Springer, 1993.

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38

Ragin, Charles C. Measurement Versus Calibration: A Set‐Theoretic Approach. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0008.

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This article distinguishes between ‘measurement’ and ‘calibration’. It is organized around the distinction between measurement and calibration. The main message of this article is that fuzzy sets, unlike conventional variables, must be calibrated. It also argues that fuzzy sets provide a middle path between quantitative and qualitative measurement. It explores the common measurement practices in quantitative and qualitative social research. It then further demonstrates that fuzzy sets resonate with both the measurement concerns of qualitative researchers, where the goal often is to recognize between relevant and irrelevant variation, and the measurement concerns of quantitative researchers, where the goal is the precise placement of cases relative to each other. Current practices in quantitative social science undercut serious attention to calibration. Set-theoretic analysis without careful calibration of set membership is an exercise in futility.
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39

Hwang, Ching-Lai, and Young-Jou Lai. Fuzzy Multiple Objective Decision Making: Methods and Applications (Lecture Notes in Economics and Mathematical Systems). Springer, 1996.

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40

Models in Cooperative Game Theory. Springer, 2008.

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41

Roventa, Eugene, and Tiberiu Spircu. Management of Knowledge Imperfection in Building Intelligent Systems. Springer, 2010.

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42

Management Of Knowledge Imperfection In Building Intelligent Systems. Springer, 2008.

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43

Bělohlávek, Radim, Joseph W. Dauben, and George J. Klir. Prehistory, Emergence, and Evolution of Fuzzy Logic. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190200015.003.0002.

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This chapter first identifies the rare instances throughout the long history of classical logic when the principle of bivalence was challenged and shows that they all have been rather inconsequential. It then briefly examines the early research on many-valued logics during the first half the twentieth century, and describes in some detail circumstances that led to the emergence of fuzzy set theory and fuzzy logic in the mid-1960s. This is followed by characterizing the evolving attitudes toward fuzzy logic, especially within the academic community, and by a summary of major and well-documented debates between members of the emerging fuzzy logic community and opponents of fuzzy logic. Finally, the chapter describes how the supporting infrastructure for fuzzy logic evolved during the early and rather critical stage of development of fuzzy logic.
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44

Fuller, Robert, Christer Carlsson, and M. Fedrizzi. Fuzzy Logic in Management (International Series in Operations Research & Management Science). Springer, 2003.

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45

Belohlavek, Radim, Joseph W. Dauben, and George J. Klir. Fuzzy Logic and Mathematics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190200015.001.0001.

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The term “fuzzy logic” (FL) is a generic one, which stands for a broad variety of logical systems. Their common ground is the rejection of the most fundamental principle of classical logic—the principle of bivalence—according to which each declarative sentence has exactly two possible truth values—true and false. Each logical system subsumed under FL allows for additional, intermediary truth values, which are interpreted as degrees of truth. These systems are distinguished from one another by the set of truth degrees employed, its algebraic structure, truth functions chosen for logical connectives, and other properties. The book examines from the historical perspective two areas of research on fuzzy logic known as fuzzy logic in the narrow sense (FLN) and fuzzy logic in the broad sense (FLB), which have distinct research agendas. The agenda of FLN is the development of propositional, predicate, and other fuzzy logic calculi. The agenda of FLB is to emulate commonsense human reasoning in natural language and other unique capabilities of human beings. In addition to FL, the book also examines mathematics based on FL. One chapter in the book is devoted to overviewing successful applications of FL and the associated mathematics in various areas of human affairs. The principal aim of the book is to assess the significance of FL and especially its significance for mathematics. For this purpose, the notions of paradigms and paradigm shifts in science, mathematics, and other areas are introduced and employed as useful metaphors.
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46

Ran, Hirschl. 6 Case Selection and Research Design in Comparative Constitutional Studies. Oxford University Press, 2014. http://dx.doi.org/10.1093/acprof:oso/9780198714514.003.0007.

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Comparative constitutional law’s methodological matrix is fuzzy and amorphous. A close look at social science methods suggests a toolkit of considerations to be addressed in conducting comparative constitutional inquiry, thus supporting various types of comparative constitutional studies. The meanings, purposes, and modes of comparative inquiry in contemporary comparative constitutional studies are identified; some basic principles of case selection and research design employed in inference-oriented small-N studies are presented; and the emerging world of multivariate, large-N studies is explored. It is argued that no research method enjoys an a priori advantage over another without taking into account the scope and nature of the studied phenomenon or the question the research purports to address. Thus, attempts to outline an “official” comparative method are not only unrealistic but also unwise. Comparative constitutionalists should settle instead on a set of several more sensible guiding principles, common rules of casuality, and a multi-method approach.
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47

Ruan, Da, Pierre D'Hondt, and Paul Govaerts. Fuzzy Logic and Intelligent Technologies in Nuclear Science: Proceedings of the 1st International Flins Workshop, Mol, Belgium, September 14-16, 199. World Scientific Publishing Company, 1995.

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