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1

United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. National Aeronautics and Space Administration, 1994.

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2

Melin, Patricia, Oscar Castillo, Janusz Kacprzyk, Marek Reformat, and William Melek, eds. Fuzzy Logic in Intelligent System Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67137-6.

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3

Dongbo, Liu, ed. A fuzzy PROLOG database system. Research Studies Press, 1990.

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4

Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. 2nd ed. World Scientific, 2007.

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5

Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. World Scientific, 1997.

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6

Ahmed, Jameel, Mohammed Yakoob Siyal, Shaheryar Najam, and Zohaib Najam. Fuzzy Logic Based Power-Efficient Real-Time Multi-Core System. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3120-5.

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7

Castillo, Oscar, and Patricia Melin, eds. Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22042-5.

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8

Jamshidi, Mohammad. Large-scale systems: Modeling, control, and fuzzy logic. Prentice Hall, 1997.

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9

Karr, C. L. An adaptive system for process control. U.S. Dept. of the Interior, Bureau of Mines, 1995.

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10

Karr, C. L. An adaptive system for process control. U.S. Dept. of the Interior, Bureau of Mines, 1995.

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11

J, Gentry E., and Stanley D. A, eds. An adaptive system for process control. U.S. Dept. of the Interior, Bureau of Mines, 1995.

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12

Neelanarayanan, ed. Multi-step Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS). Association of Scientists, Developers and Faculties, 2014.

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13

C, Rapp Douglas, and United States. National Aeronautics and Space Administration., eds. Rocket engine system reliability analyses using probabilistic and fuzzy logic techniques. National Aeronautics and Space Administration, 1994.

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14

M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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15

M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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16

Demri, Stéphane P. Incomplete information: Structure, inference, complexity. Springer, 2002.

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17

Chernichovsky, Dov. A fuzzy logic approach toward solving the analytic maze of health system financing. National Bureau of Economic Research, 2001.

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18

Horia-Nicolai, Teodorescu, Kandel Abraham, and Jain L. C, eds. Fuzzy and neuro-fuzzy systems in medicine. CRC Press, 1999.

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19

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

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20

Castillo, Oscar, and Patricia Melin, eds. New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08266-5.

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21

1843-1914, Lindsay Thomas M., ed. System of logic and history of logical doctrines. Thoemmes Press, 1993.

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22

Robyns, Benoit. Vector Control of Induction Machines: Desensitisation and Optimisation Through Fuzzy Logic. Springer London, 2012.

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23

Matviĭchuk, A. V. Analiz ta prohnozuvanni︠a︡ rozvytku finansovo-ekonomichnykh system iz vykorystanni︠a︡m teoriï nechitkoï lohiky. T︠S︡entr navchalʹnoï literatury, 2005.

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24

Relander, Sami. Towards approximate reasoning on new software product company success potential estimation: A design science based fuzzy logic expert system. Helsinki School of Economics, 2008.

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25

Symposium on System Modelling, Fault Diagnosis, and Fuzzy Logic and Control (1997 Budapest and Miskolc, Hungary). Proceedings of the Symposium on System Modelling, Fault Diagnosis, and Fuzzy Logic and Control: Budapest and Miskolc, Hungary, May 6-10, 1997. Technical University of Budapest, 1997.

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26

Symposium on Qualitative System Modelling, Qualitative Fault Diagnosis and Fuzzy Logic and Control (1996 Budapest and Balatonfüred, Hungary). Proceedings of symposium on qualitative system modelling, qualitative fault diagnosis and fuzzy logic and control: April 17-20, 1996, Budapest and Balatonfüred, Hungary. [s.n.], 1996.

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27

Artificial, Neural Networks in Engineering Conference (2003 St Louis Mo ). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, complex systems and artificial life : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2003) : held November 2-5, 2003, in St. Louis, Missouri, U.S.A. ASME Press, 2003.

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28

Artificial, Neural Networks in Engineering Conference (11th 2001 St Louis Missouri). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, data mining and complex systems : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2001), held November 4-7, 2001, in St. Louis, Missouri, U.S.A. ASME Press, 2001.

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29

1949-, Dagli Cihan H., ed. Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, data mining and complex systems : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE '99), held November 7-10, 1999, in St. Louis, Missouri, U.S.A. ASME Press, 1999.

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30

Artificial Neural Networks in Engineering Conference (10th 2000 St. Louis, Missouri). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, data mining and complex systems : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2000), held November 5-8, 2000, in St. Louis, Missouri, U.S.A. Edited by Dagli Cihan H. 1949-. ASME Press, 2000.

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31

Artificial Neural Networks in Engineering Conference (13th 2004 St. Louis, Missouri). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, complex systems and artificial life : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2004), held November 7-10, 2004, in St. Louis, Missouri, U.S.A. Edited by Dagli Cihan H. 1949-. ASME Press, 2004.

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32

1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics, and Cybernetics. (9th : 1997 : Baden-Baden, Germany), eds. Advances in artificial intelligence and engineering cybernetics: Systems logic and neural networks, theory and applications of AI methods, present status of general system theory, formal representation of meaning in natural languages, inductive and deductive reasoning logic, dynamic fuzzy sets and fuzzy control, multiple valued stepwise logic networks, computer animated actors with intelligent agents, neural activity and synaptic connectivity in neural networks, information coding and neural computing, modelling by neural networks, engineering applications of artificial neural networks /cedited by George E. Lasker. International Institute for Advanced Studies in Systems Research and Cybernetics, 1998.

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33

Leigh, J. R. Control Theory. 2nd ed. IET, 2004.

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34

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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35

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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36

Elements of Causal Inference. The MIT Press, 2017.

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37

Fuzzy Inference System. NY RESEARCH PRESS, 2015.

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38

Azeem, Mohammad Fazle, ed. Fuzzy Inference System - Theory and Applications. InTech, 2012. http://dx.doi.org/10.5772/2341.

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39

Niemiec, Mariusz. Fuzzy Inference System - Theory and Applications. Scitus Academics LLC, 2017.

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40

Zhu, A.-Xing. Soil pattern inference using GIS under fuzzy logic. 1994.

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41

Uncu, Özge. Type 2 fuzzy system models with type 1 inference. 2003.

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42

Chakraverty, Snehashish, Smita Tapaswini, and Diptiranjan Behera. Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications. Wiley & Sons, Incorporated, John, 2016.

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43

Chakraverty, Snehashish, Smita Tapaswini, and Diptiranjan Behera. Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications. Wiley & Sons, Limited, John, 2016.

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44

Chakraverty, Snehashish, Smita Tapaswini, and Diptiranjan Behera. Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications. Wiley & Sons, Limited, John, 2016.

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45

Chakraverty, Snehashish, Smita Tapaswini, and Diptiranjan Behera. Fuzzy Arbitrary Order System: Fuzzy Fractional Differential Equations and Applications. Wiley & Sons, Incorporated, John, 2016.

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46

Gergely, Tamas, Oleg M. Anshakov, Victor K. Finn, and Sergei O. Kuznetsov. Cognitive Research. Springer, 2006.

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47

Fuzzy Logic: Applications, Systems and Technologies. Nova Novinka, 2013.

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48

Guo, Lizhu. Key optimization issues for the design of fuzzy-inference system. 1997.

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49

Najam, Shaheryar, Zohaib Najam, Jameel Ahmed, and Mohammed Yakoob Siyal. Fuzzy Logic Based Power-Efficient Real-Time Multi-Core System. Springer Singapore Pte. Limited, 2016.

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50

Najam, Shaheryar, Zohaib Najam, Jameel Ahmed, and Mohammed Yakoob Siyal. Fuzzy Logic Based Power-Efficient Real-Time Multi-Core System. Springer, 2017.

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