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Books on the topic 'Fuzzy rule based methods'

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Published: 4 June 2025

Last updated: 15 July 2025

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1

Li, Fangyi, and Qiang Shen. Fuzzy Rule-Based Inference. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0491-0.

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2

Mendel, Jerry M. Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51370-6.

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3

Mendel, Jerry M. Explainable Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-35378-9.

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4

Mendel, Jerry M. Uncertain rule-based fuzzy logic systems: Introduction and new directions. Prentice Hall, 2001.

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5

Ebrahimnejad, Ali, and José Luis Verdegay. Fuzzy Sets-Based Methods and Techniques for Modern Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73903-8.

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6

Geyer-Schulz, Andreas. Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, 1995.

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7

Bárdossy, András. Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems. CRC Press, 1995.

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8

Gonzalez, Claudia I., Patricia Melin, Juan R. Castro, and Oscar Castillo. Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53994-2.

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9

Angelov, Plamen P. Evolving rule-based models: A tool for design of flexible adaptive systems. Physica-Verlag, 2002.

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10

B, Sidor L., United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., and Dryden Flight Research Facility, eds. Rule-based air combat simulation. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1988.

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11

B, Sidor L., United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., and Dryden Flight Research Facility, eds. Rule-based air combat simulation. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1988.

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12

B, Sidor L., United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., and Dryden Flight Research Facility, eds. Rule-based air combat simulation. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1988.

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13

S, Teichrow Jon, University of Houston--Clear Lake. Research Institute for Computing and Information Systems., and Lyndon B. Johnson Space Center. Information Technology Division., eds. Real-time fuzzy inference based robot path planning: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.

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14

Wan, Shuping, and Jiuying Dong. Decision Making Theories and Methods Based on Interval-Valued Intuitionistic Fuzzy Sets. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1521-7.

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15

Akec, John Apuruot. Optimisation methods for fuzzy logic based control systems for use in robotics. University of Birmingham, 1999.

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16

Tian, Xiaoli, and Zeshui Xu. Fuzzy Decision-Making Methods Based on Prospect Theory and Its Application in Venture Capital. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0243-6.

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17

Angelov, Plamen P. Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Physica-Verlag HD, 2002.

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18

Aliev, Rafik Aziz. Fundamentals of the Fuzzy Logic-Based Generalized Theory of Decisions. Springer Berlin Heidelberg, 2013.

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19

Bobyr', Maksim, Sergey Emel'yanov, and Natal'ya Milostnaya. Automated fuzzy logic control systems and methods of their sensitization. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2162831.

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The monograph is devoted to the methods of designing automated fuzzy logic control systems and methods of their sensing using vision systems. The presented methods, mathematical models, algorithms, and fuzzy logic control systems based on them differ from the known ones by automatically correcting the parameters of the cutting mode, taking into account the disturbing effects acting on the control object in real time. It is addressed to scientists, engineers, graduate students and students of technical specialties interested in fuzzy logic, control theory, stability of complex systems and management of intelligent systems based on machine vision systems. The work is based on scientific projects partially supported by the State Assignment No. 075-03-2025-526 and the Russian Science Foundation No. 24-21-00055.

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20

Dubois, Didier. Systèmes à bases de règles floues et leurs applications pour la décision et la commande =: Fuzzy rule-based systems andapplications to decision and control. Institut de Recherche en Informatique de Toulouse, 1991.

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21

Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. 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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22

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" indicators of safety and risk of technical systems. Using typical examples, the methods and results of solving the stated tasks are considered. For students, postgraduates and teachers of technical and technological universities and faculties, as well as for a wide range of readers interested in technosphere security issues.

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23

Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".  The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.  Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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24

Uncertain Rule-Based Fuzzy Systems. Springer International Publishing AG, 2023.

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25

Uncertain Rule-Based Fuzzy Systems. Springer Nature, 2017.

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26

Fuzzy rule based computer design. CRC Press, 1996.

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27

Mendel, Jerry M. Introduction to Rule-Based Fuzzy Logic Systems. Ieee, 2001.

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28

Mendel, Jerry M. Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd Edition. Springer, 2018.

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29

Mendel, Jerry M. Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd Edition. Springer, 2017.

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30

Ebrahimnejad, Ali. Fuzzy Sets-Based Methods and Techniques for Modern Analytics. Springer, 2018.

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31

Ebrahimnejad, Ali, and José Luis Verdegay. Fuzzy Sets-Based Methods and Techniques for Modern Analytics. Springer, 2018.

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32

Gonzalez, Claudia I., Oscar Castillo, Patricia Melin, and Juan R. Castro. Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. Springer International Publishing AG, 2017.

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33

Mendel, Jerry M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR, 2000.

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34

Mendel, Jerry M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR, 2000.

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35

Fuzzy rule-based expert systems and genetic machine learning. 2nd ed. Physica-Verlag, 1997.

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36

Serodio, Carlos, Pedro Couto, Pedro Melo-Pinto, János Fodor, and Bernard De Baets. Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems. Springer, 2011.

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37

Mendel, Jerry M. New Directions in Rule-Based Fuzzy Logic Systems: Handling Uncertainties. Inst Elect & Electronic Engineers, 2001.

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38

Real-time fuzzy inference based robot path planning: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.

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39

Sadollah, Ali, ed. Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications. InTech, 2018. http://dx.doi.org/10.5772/intechopen.73112.

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40

Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications. IntechOpen, 2018.

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41

Wan, Shuping, and Jiuying Dong. Decision Making Theories and Methods Based on Interval-Valued Intuitionistic Fuzzy Sets. Springer, 2020.

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42

Bardossy, Andras, and Lucien Duckstein. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems. Taylor & Francis Group, 2022.

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43

Bardossy, Andras, and Lucien Duckstein. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems. Taylor & Francis Group, 2022.

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44

Bardossy, Andras, and Lucien Duckstein. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems. Taylor & Francis Group, 2022.

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45

Wan, Shuping, and Jiuying Dong. Decision Making Theories and Methods Based on Interval-Valued Intuitionistic Fuzzy Sets. Springer, 2020.

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46

Wan, Shuping, and Jiuying Dong. Decision Making Theories and Methods Based on Interval-Valued Intuitionistic Fuzzy Sets. Springer Singapore Pte. Limited, 2021.

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47

Bàrdossy, András. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems. 2022.

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48

Fuzzy Rule-Based Inference: Advances and Applications in Reasoning with Approximate Knowledge Interpolation. Springer, 2024.

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49

Tian, Xiaoli, and Zeshui Xu. Fuzzy Decision-Making Methods Based on Prospect Theory and Its Application in Venture Capital. Springer Singapore Pte. Limited, 2021.

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50

Tian, Xiaoli, and Zeshui Xu. Fuzzy Decision-Making Methods Based on Prospect Theory and Its Application in Venture Capital. Springer Singapore Pte. Limited, 2022.

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