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Books on the topic 'Fuzzy logic method'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Amezcua, Jonathan, Patricia Melin, and Oscar Castillo. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73773-7.

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2

Siegfried, Gottwald, ed. Fuzzy sets, fuzzy logic, fuzzy methods with applications. J. Wiley, 1995.

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3

Arfi, Badredine. Linguistic Fuzzy Logic Methods in Social Sciences. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13343-5.

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4

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 manag

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5

IGOShIN, Vladimir. Logic with elements of mathematical logic. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1856361.

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In the textbook, traditional logic is presented from the point of view of mathematical logic. Mathematization begins with the study of the topic "Concept", continues with the topic "Judgment" and reaches its greatest effectiveness in the study of deductive reasoning. Thus, the most effective part of traditional logic is subjected to mathematization. The issues of the relationship between logic and intuition in thought processes, the role of language in them, as well as plausible conclusions and elements of the theory of fuzzy sets and fuzzy logic are considered. Meets the requireme

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6

United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. National Aeronautics and Space Administration, 1994.

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7

United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. National Aeronautics and Space Administration, 1994.

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8

Cios, Krzysztof J. A comparison of neural networks and fuzzy logic methods for process modeling. National Aeronautics and Space Administration, 1996.

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9

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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10

1976-, Laurent Anne, and Lesot Marie-Jeanne 1978-, eds. Scalable fuzzy algorithms for data management and analysis: Methods and design. Information Science Reference, 2009.

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11

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

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12

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

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13

Taylor, Cynthia. A programmer's guide to the fuzzy logic ramp metering algorithm: Software design, integration, testing, and evaluation. Washington State Dept. of Transportation, 2000.

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14

M, Ayyub Bilal, and Gupta Madan M, eds. Uncertainty analysis in engineering and sciences: Fuzzy logic, statistics, and neural network approach. Kluwer Academic Publishers, 1998.

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15

Ayyub, Bilal M. Uncertainty analysis in engineering and sciences: Fuzzy logic, statistics, and neural network approach. Kluwer Academic Publishers, 1997.

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16

Taylor, Cynthia. Algorithm design, user interface, and optimization procedure for a fuzzy logic ramp metering algorithm: A training manual for freeway operations engineers. Washington State Dept. of Transportation, 2000.

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17

Taylor, Cynthia. Evaluation of a fuzzy logic ramp metering algorithm: A comparative study among three ramp metering algorithms used in the greater Seattle area. Washington State Dept. of Transportation, 2000.

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18

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

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19

Silov, V. B. Prini͡a︡tie strategicheskikh resheniĭ v nechetkoĭ obstanovke: V politike, makroėkonomike, sot͡s︡iologii, menedzhmente, medit͡s︡ine, ėkologii. INPRO-RES, 1995.

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20

1957-, Lu Jie, ed. Multi-objective group decision making: Methods, software and applications with fuzzy set techniques. Imperial College Press, 2007.

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21

Maji, Pradipta. Rough-fuzzy pattern recognition: Applications in bioinformatics and medical imaging. Wiley, 2011.

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22

K, Sweby P., and Ames Research Center, eds. Nonlinear dynamics & numerical uncertainties in CFD. National Aeronautics and Space Administration, Ames Research Center, 1996.

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23

K, Sweby P., and Ames Research Center, eds. Nonlinear dynamics & numerical uncertainties in CFD. Ames Research Center, 1996.

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24

K, Sweby P., and Ames Research Center, eds. Nonlinear dynamics & numerical uncertainties in CFD. National Aeronautics and Space Administration, Ames Research Center, 1996.

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25

Ching-man, Au Yeung, and Leung Ho-Fung, eds. Fuzzy computational ontologies in contexts: Formal models of knowledge representation with membership degree and typicality of objects, and their applications. Higher Education Press, 2012.

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26

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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27

Amezcua, Jonathan. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. Springer, 2018.

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28

Chaos theory in the financial markets: Applying fractals, fuzzy logic, genetic algorithms, swarm simulation & the Monte Carlo method to manage market chaos & volatility. Probus Pub. Co., 1994.

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29

Ross, Timothy. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2009.

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30

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2009.

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31

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2016.

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32

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Limited, John, 2010.

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33

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2010.

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34

J, Ross T., and Timothy J. Ross. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2005.

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35

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2008.

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36

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2016.

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37

Ross, Timothy J. Fuzzy Logic with Engineering Applications. Wiley & Sons, Incorporated, John, 2016.

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38

Viertl, Reinhard. Statistical Methods for Fuzzy Data. Wiley & Sons, Incorporated, John, 2010.

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39

Viertl, Reinhard. Statistical Methods for Fuzzy Data. Wiley & Sons, Limited, John, 2011.

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40

Viertl, Reinhard. Statistical Methods for Fuzzy Data. Wiley & Sons, Incorporated, John, 2011.

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41

Viertl, Reinhard. Statistical Methods for Fuzzy Data. Wiley & Sons, Limited, John, 2010.

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42

Viertl, Reinhard. Statistical Methods for Fuzzy Data. Wiley & Sons, Limited, John, 2011.

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43

Dostál, Petr, and Chia-Yang Lin. Business Applications of Fuzzy Logic. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.14.

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The chapter focuses on the use of fuzzy logic, or soft computing, among the different methods used as supports for decision making in business applications. The processes are focused on private corporate attempts at making money or decreasing expenses; therefore, the details of applications, successful or not, are not published very often. Fuzzy logic helps in decentralization of decisionmaking processes that are to be standardized, reproduced, and documented. Fuzzy logic plays very important roles, especially in business, because it helps reduce costs. It differs from conventional (hard) comp

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44

Fuzzy logic with engineering applications. 3rd ed. John Wiley, 2010.

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45

Fuzzy Logic with Engineering Applications. Wiley & Sons, Limited, John, 2016.

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46

Fuzzy Logic with Engineering Applications. McGraw-Hill Education (ISE Editions), 1994.

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47

Linguistic fuzzy logic methods in social sciences. Springer, 2010.

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48

Arfi, Badredine. Linguistic Fuzzy Logic Methods in Social Sciences. Springer Berlin / Heidelberg, 2014.

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49

Arfi, Badredine. Linguistic Fuzzy Logic Methods in Social Sciences. Springer London, Limited, 2010.

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50

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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