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Published: 11 September 2023

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1

Kolman, Eyal, and Michael Margaliot. Knowledge-Based Neurocomputing: A Fuzzy Logic Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88077-6.

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2

Michael, Margaliot, ed. Knowledge-based neurocomputing: A fuzzy logic approach. Berlin: Springer, 2009.

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3

Multistage fuzzy control: A model-based approach to fuzzy control and decision making. Chichester: Wiley, 1997.

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4

Feng, Gang. Analysis and synthesis of fuzzy control systems: A model-based approach. Boca Raton: CRC Presss, 2010.

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5

Analysis and synthesis of fuzzy control systems: A model based approach. Boca Raton: CRC Presss, 2010.

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6

Controlling accidents and insurers' risks in construction: A fuzzy knowledge-based approach. Hauppauge, NY: Nova Science Publishers, 2009.

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7

United States. National Aeronautics and Space Administration., ed. Fuzzy sets, rough sets, and modeling evidence: Theory and application : a Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection. [Clear Lake, Tex.]: Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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8

Korvin, Andre De. Fuzzy sets, rough sets, and modeling evidence: Theory and application : a Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection. [Clear Lake, Tex.]: Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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9

United States. National Aeronautics and Space Administration., ed. Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results. [Houston, Tex.]: Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1993.

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10

Gegov, Alexander. Fuzzy Networks for Complex Systems: A Modular Rule Base Approach. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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11

Marios, Polycarpou, ed. Adaptive approximation based control: Unifying neural, fuzzy and traditional adaptive approximation approaches. Hoboken, NJ: Wiley, 2006.

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12

Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. Florence: Firenze University Press, 2002. http://dx.doi.org/10.36253/88-8453-042-3.

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Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment.
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13

Margaliot, Michael, and Eyal Kolman. Knowledge-Based Neurocomputing: A Fuzzy Logic Approach. Springer Berlin / Heidelberg, 2010.

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14

Kacprzyk, Janusz. Multistage Fuzzy Control: A Model-Based Approach to Fuzzy Control and Decision Making. Wiley & Sons, Limited, John, 2009.

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15

Multistage Fuzzy Control: A Model-Based Approach to Fuzzy Control and Decision Making. Wiley & Sons, Incorporated, John, 2000.

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16

Kacprzyk, Janusz. Multistage Fuzzy Control: A Model-Based Approach to Fuzzy Control and Decision Making. Wiley & Sons, Limited, John, 2022.

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17

Chatterjee, Amitava, Anjan Rakshit, and Kaushik Das Sharma. Intelligent Control: A Stochastic Optimization Based Adaptive Fuzzy Approach. Springer, 2018.

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18

Chatterjee, Amitava, Anjan Rakshit, and Kaushik Das Sharma. Intelligent Control: A Stochastic Optimization Based Adaptive Fuzzy Approach. Springer, 2018.

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19

Chatterjee, Amitava, Anjan Rakshit, and Kaushik Das Sharma. Intelligent Control: A Stochastic Optimization Based Adaptive Fuzzy Approach. Springer, 2018.

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20

Trajkovski, Goran. Imitation-Based Approach to Modeling Homogenous Agents Societies. IGI Global, 2006.

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21

Feng, Gang. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. Taylor & Francis Group, 2018.

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22

Feng, Gang. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. Taylor & Francis Group, 2018.

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23

Feng, Gang. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. Taylor & Francis Group, 2010.

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24

Feng, Gang. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. Taylor & Francis Group, 2018.

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25

Feng, Gang. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. Taylor & Francis Group, 2018.

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26

Feng, Gang. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach. Taylor & Francis Group, 2017.

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27

Lam, Hak-Keung, and Allen Leung. Stability Analysis of Fuzzy-Model-Based Control Systems: Linear-Matrix-Inequality Approach. Springer, 2014.

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28

Singh, Bhupinder, Jagdeep Singh, and Harwinder Singh. Prioritization of Failure Modes in Manufacturing Processes: A Fuzzy Logic-Based Approach. Emerald Publishing Limited, 2020.

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29

Kamardeen, Imriyas. Controlling Accidents and Insurers' Risks in Construction: A Fuzzy Knowledge-Based Approach. Nova Science Publishers, Incorporated, 2009.

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30

Lam, Hak-Keung, and Allen Leung. Stability Analysis of Fuzzy-Model-Based Control Systems: Linear-Matrix-Inequality Approach. Springer London, Limited, 2011.

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31

Singh, Bhupinder, Jagdeep Singh, and Harwinder Singh. Prioritization of Failure Modes in Manufacturing Processes: A Fuzzy Logic-Based Approach. Emerald Publishing Limited, 2020.

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32

Singh, Bhupinder, Jagdeep Singh, and Harwinder Singh. Prioritization of Failure Modes in Manufacturing Processes: A Fuzzy Logic-Based Approach. Emerald Publishing Limited, 2020.

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33

Tucker, Joseph C. A software-based approach to image stabilization for a camera mounted on a moving vehicle. 1993.

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34

Siddique, Nazmul. Intelligent Control: A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms. Springer London, Limited, 2013.

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35

Siddique, Nazmul. Intelligent Control: A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms. Springer, 2016.

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36

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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37

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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38

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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39

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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40

Trajkovski, Goran. An Imitation-based Approach to Modeling Homogenous Agents Societies (Computational Intelligence and Its Applications Series) (Computational Intelligence and Its Applications Series). Idea Group Publishing, 2006.

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41

Chung, Edwin. A proposed intelligent bandwidth management system based on Turksen's Fuzzy Function approach using reinforcement learning forecasting. 2005.

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42

Value Determination Of Supply Chain Initiatives A Quantification Approach Based On Fuzzy Logic And System Dynamics. Gabler Verlag, 2010.

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43

Chung, Edwin. A proposed intelligent bandwidth management system based on Turksen's Fuzzy Function approach using reinforcement learning forecasting. 2005.

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44

Wessely, Philip. Value Determination of Supply Chain Initiatives: A Quantification Approach Based on Fuzzy Logic and System Dynamics. Westdeutscher Verlag GmbH, 2010.

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45

Scheipers, Ulrich. Sonohistology. Methods and Systems for Ultrasonic Tissue Characterization Based on a Multifeature Approach and Fuzzy Inference Systems. Logos Verlag Berlin, 2005.

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46

Bělohlávek, Radim, Joseph W. Dauben, and George J. Klir. Mathematics Based on Fuzzy Logic. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190200015.003.0005.

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Mathematical reasoning is governed by the laws of classical logic, based on the principle of bivalence. With the acceptance of intermediate truth degrees, the situation changed substantially. This chapter begins with a characterization of mathematics based on fuzzy logic, an identification of principal issues of its development, and an outline of this development. It then examines the role of fuzzy logic in the narrow sense for developing mathematics based on fuzzy logic and the main approaches developed toward its foundations. Next, some selected areas of mathematics based on fuzzy logic are presented, such as the theory of sets and relations, algebra, topology, quantities and mathematical analysis, probability, and geometry. The chapter concludes by examining various semantic questions regarding fuzzy logic and mathematics based on it.
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47

Gegov, Alexander. Complexity Management in Fuzzy Systems: A Rule Base Compression Approach. Springer Berlin / Heidelberg, 2010.

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48

Gegov, Alexander. Complexity Management in Fuzzy Systems: A Rule Base Compression Approach. Springer London, Limited, 2007.

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49

Gegov, Alexander. Fuzzy Networks for Complex Systems: A Modular Rule Base Approach. Springer London, Limited, 2012.

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50

Farrell, Jay A., and Marios M. Polycarpou. Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches. Wiley & Sons Australia, Limited, John, 2006.

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