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1

Scherer, Rafał. Multiple Fuzzy Classification Systems. Springer Berlin Heidelberg, 2012.

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2

A, Ralescu D., ed. Simulation, knowledge-based computing, and fuzzy statistics. Van Nostrand Reinhold, 1987.

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3

N, Chorafas Dimitris. Financial models and simulation. St. Martins, 1995.

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4

Chorafas, Dimitris N. Financial models and simulation. Macmillan, 1995.

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5

Taylor, Cynthia E. Simulation testing of a fuzzy neural ramp metering algorithm. Washington State Dept. of Transportation, 1995.

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6

Rotshtein, Alexander P. Fuzzy Evidence in Identification, Forecasting and Diagnosis. Springer Berlin Heidelberg, 2012.

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7

Kacprzyk, Janusz, and Oscar Castillo. Evolutionary design of intelligent systems in modeling, simulation and control. Springer, 2009.

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8

United States. National Aeronautics and Space Administration., ed. NASA grant NCC2-275: Final progress report. National Aeronautics and Space Administration, 1998.

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9

Starczewski, Janusz T. Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Springer Berlin Heidelberg, 2013.

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10

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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11

I, Ruban A., ed. Imitat͡s︡ionnye modeli neopredelennykh sistem. VO ʺNaukaʺ, 1993.

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12

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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13

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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14

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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15

Nelles, Oliver. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer Berlin Heidelberg, 2001.

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16

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

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17

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

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18

J. Buckley, James. Simulating Fuzzy Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b100371.

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19

J, Buckley James. Simulating fuzzy systems. Springer, 2004.

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20

Tucci, Mario, and Marco Garetti, eds. Proceedings of the third International Workshop of the IFIP WG5.7. 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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21

IEEE International Conference on Fuzzy Systems (10th 2001 Melbourne, Australia). The 10th IEEE International Conference on Fuzzy Systems: Meeting the grand challenge : machines that serve people : December, 2001, Sunday 2nd to Wednesday 5th. IEEE, 2001.

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22

1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (7th : 1994 : Baden-Baden, Germany), eds. Advances in computer cybernetics: Intelligent information systems, knowledge transmission and representation, software development, self-organizing production processes, fuzzy systems, virtual environment, advanced computer graphics, synesthetic art, advanced simulation, neural networks, intelligent control, advanced robotics. International Institute for Advanced Studies in Systems Research and Cybernetics, 1995.

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23

Simulating fuzzy systems. Springer, 2005.

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24

Scherer, Rafał. Multiple Fuzzy Classification Systems. Springer, 2012.

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25

J, Buckley James, and Leonard J. Jowers. Simulating Continuous Fuzzy Systems. Springer London, Limited, 2008.

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26

J, Buckley James, and Leonard J. Jowers. Simulating Continuous Fuzzy Systems. Springer Berlin / Heidelberg, 2010.

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27

Chorafas, Dimitris, and Chorafas Dr Dimitris N. Financial Models and Simulation. Palgrave Macmillan, 1995.

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28

Chorafas, D. Financial Models and Simulation. Palgrave Macmillan, 1995.

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29

Multiple Fuzzy Classification Systems. Springer, 2012.

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30

Scherer, Rafa. Multiple Fuzzy Classification Systems. Springer Berlin / Heidelberg, 2014.

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31

Starczewski, Janusz T. Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Springer, 2012.

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32

Goyal, Neeraj Kumar, and Ajeet Kumar Pandey. Early Software Reliability Prediction: A Fuzzy Logic Approach. Springer, 2015.

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33

Goyal, Neeraj Kumar, and Ajeet Kumar Pandey. Early Software Reliability Prediction: A Fuzzy Logic Approach. Springer London, Limited, 2013.

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34

Rakytyanska, Hanna B., and Alexander P. Rotshtein. Fuzzy Evidence in Identification, Forecasting and Diagnosis. Springer, 2014.

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35

Vermeir, Dirk, Steven Schockaert, Martine De Cock, and Jeroen Janssen. Answer Set Programming for Continuous Domains: A Fuzzy Logic Approach. Atlantis Press (Zeger Karssen), 2014.

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36

Answer Set Programming for Continuous Domains: A Fuzzy Logic Approach. Springer, 2012.

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37

Starczewski, Janusz T. Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Springer, 2014.

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38

Sirbiladze, Gia. Extremal Fuzzy Dynamic Systems: Theory and Applications. Springer, 2014.

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39

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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40

Singh, Pritpal. Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques. Springer International Publishing AG, 2016.

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41

Singh, Pritpal. Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques. Springer International Publishing AG, 2015.

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42

Singh, Pritpal. Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques. Springer, 2015.

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43

Bernstein, Herbert. PC- Simulation der Regelungstechnik und Fuzzy- Logik. Entwurf und Synthese elektronischer Systeme. VDE-Verlag, 1998.

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44

Control Estimation and Optimization for Microgrids: A Fuzzy-Model-based Method. Taylor & Francis Group, 2019.

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45

Castillo, Oscar, and Witold Pedrycz. Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control. Springer, 2012.

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46

Hellendoorn, Hans, and Dimiter Driankov. Fuzzy Model Identification: Selected Approaches. Springer London, Limited, 2012.

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47

(Editor), Hans Hellendoorn, and Dimiter Driankov (Editor), eds. Fuzzy Model Identification: Selected Approaches. Springer, 1998.

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48

Hoang, Patrick. Design and analysis of an adaptive fuzzy power system stabilizer. 1994.

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49

Fuzzy Cognitive Maps: A Tool for the Modeling and Simulation of Processes and Systems. Springer, 2023.

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50

Nelles, Oliver. Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes. Springer International Publishing AG, 2021.

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