Abstract
The biggest success of fuzzy systems in the field of industrial and commercial applications has been achieved with fuzzy controllers. Fuzzy control is a way of defining a nonlinear table-based controller whereas its nonlinear transition function can be defined without specifying every single entry of the table individually. Fuzzy control does not result from classical control engineering approaches. In fact, its roots can be found in the area of rule-based systems.
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References
A.G. Barto, R.S. Sutton, C.W. Anderson, Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. 13(5), 834–846. (IEEE Press, Piscataway, NJ, USA, 1983)
H.R. Berenji, P. Khedkar, Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Netw. 3(5), 724–740. (IEEE Press, Piscataway, NJ, USA, 1992)
S.K. Halgamuge, M. Glesner, Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets Syst. 65(1), 1–12. (Elsevier, Amsterdam, Netherlands, 1994)
J. Hopf, F. Klawonn, Learning the rule base of a fuzzy controller by a genetic algorithm, in Fuzzy Systems in Computer Science, 63–74, ed. by R. Kruse, J. Gebhardt, R. Palm Vieweg, Braunschweig, Germany (1994)
J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23(3):665–685. (IEEE Press, Piscataway, NJ, USA, 1993)
L.P. Kaelbling, M.H. Littman, A.W. Moore, Reinforcement learning: a survey, J. Artif. Intell. Res. 4:237–285. (AI Access Foundation and Morgan Kaufman Publishers, El Segundo/San Francisco, CA, USA, 1996)
J. Kahlert, H. Frank, Fuzzy-Logik und Fuzzy-Control, 2nd edition (in German) (Vieweg, Braunschweig, Germany, 1994)
J. Kinzel, F. Klawonn, R. Kruse, Modifications of Genetic Algorithms for Designing and Optimizing Fuzzy Controllers, in Proceedings of IEEE Conference on Evolutionary Computation (ICEC’94, Orlando, FL), pp. 28–33. (IEEE Press, Piscataway, NJ, USA, 1994)
F. Klawonn, On a Lukasiewicz Logic Based Controller, in Proceedings of International Seminar on Fuzzy Control through Neural Interpretations of Fuzzy Sets (MEPP’92), pp. 53–56. (Åbo Akademi, Turku, Finland, 1992)
F. Klawonn, J.L. Castro, Similarity in fuzzy reasoning. Math. Soft Comput. 2, 197–228. (University of Granada, Granada, Spain, 1995)
F. Klawonn, R. Kruse, The Inherent Indistinguishability in Fuzzy Systems, in Logic versus Approximation: Essays Dedicated to Michael M. Richter on the Occasion of his 65th Birthday ed. by W. Lenski, pp. 6–17. (Springer-Verlag, Berlin, Germany 2004)
F. Klawonn, V. Novák, The relation between inference and interpolation in the framework of fuzzy systems. Fuzzy Sets Syst. 81, 331–354 (1996). Elsevier, Amsterdam, Netherlands
B. Kosko (ed.), Neural Networks for Signal Processing (Prentice Hall, Englewood Cliffs, NJ, USA, 1992)
M. Lee, H. Takagi, Integrating Design Stages of Fuzzy Systems Using Genetic Algorithms, in Proceedings of IEEE International Conference on Fuzzy Systems (San Francisco, CA), pp. 612–617. (IEEE Press, Piscataway, NJ, USA 1993)
E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7, 1–13. (Academic Press, Waltham, MA, USA, 1975)
K. Michels, F. Klawonn, R. Kruse, A. Nürnberger, Fuzzy Control: Fundamentals, Stability and Design of Fuzzy Controllers. Studies in Fuzziness and Soft Computing, vol. 200. (Springer-Verlag, Berling/Heidelberg, Germany 2006)
C. Moewes, R. Kruse, On the Usefulness of Fuzzy SVMs and the Extraction of Fuzzy Rules from SVMs, in Proceedings of 7th Conference of Europe Society for Fuzzy Logic and Technology (EUSFLAT-2011) and LFA-2011, ed. by S. Galichet, J. Montero, and G. Mauris, Advances in Intelligent Systems Research, vol. 17, pp. 943–948. (Atlantis Press, Amsterdam/Paris, Netherlands/France, 2011)
C. Moewes, R. Kruse, Fuzzy Control for Knowledge-Based Interpolation, in Combining Experimentation and Theory: A Hommage to Abe Mamdani, ed. by E. Trillas, P.P. Bonissone, L. Magdalena, J. Kacprzyk, pp. 91–101. (Springer-Verlag, Berlin/Heidelberg, Germany, 2012)
C. Moewes, R. Kruse, Evolutionary Fuzzy Rules for Ordinal Binary Classification with Monotonicity Constraints, in Soft Computing: State of the Art Theory and Novel Applications, ed. by R.R. Yager, A.M. Abbasov, M.Z. Reformat, S.N. Shahbazova, Studies in Fuzziness and Soft Computing, vol. 291, pp. 105–112. (Springer-Verlag, Berlin/Heidelberg, Germany, 2013)
D.D. Nauck, F. Klawonn, R. Kruse, Foundations of Neuro-Fuzzy Systems (Wiley, Chichester, 1997)
D. Nauck, R. Kruse, A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation. In: Proc. IEEE Int. Conf. on Neural Networks (ICNN’93, San Francisco, CA), 1022–1027 (IEEE Press, Piscataway, NJ, USA, 1993). 1993
D.D. Nauck, A. Nürnberger, Neuro-fuzzy Systems: A Short Historical Review, in Computational Intelligence in Intelligent Data Analysis, ed. by C. Moewes, A. Nürnberger. Studies in Computational Intelligence, vol. 445, pp. 91–109. (Springer-Verlag, Berlin/Heidelberg, Germany, 2012)
H. Nomura, I. Hayashi, N. Wakami, A learning method of fuzzy inference rules by descent method, in Proceedings of IEEE International Conference on Fuzzy Systems, pp. 203–210. San Diego, CA, USA, 1992)
A. Nürnberger, D.D. Nauck, R. Kruse, Neuro-fuzzy control based on the NEFCON-model: recent developments. Soft Comput. 2(4), 168–182. (Springer-Verlag, Berlin/Heidelberg, Germany, 1999)
M. Riedmiller, M. Spott, J. Weisbrod, FYNESSE: A hybrid architecture for selflearning control, in Knowledge-Based Neurocomputing, 291–323, ed. by I. Cloete, J. Zurada (MIT Press, Cambridge, MA, USA, 1999)
T.A. Runkler. Kernel Based Defuzzification, in Computational Intelligence in Intelligent Data Analysis ed. by C. Moewes and A. Nürnberger,pp. 61–72. (Springer-Verlag, Berlin/Heidelberg, Germany, 2012)
T.A. Runkler, M. Glesner, A Set of Axioms for Defuzzification Strategies — Towards a Theory of Rational Defuzzification Operators, in Proceedings of 2nd IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’93, San Francisco, CA), pp. 1161–1166. (IEEE Press, Piscataway, NJ, USA, 1993)
M. Sugeno, An introductory survey of fuzzy control. Inf. Sci. 36:59–83. (Elsevier, New York, NY, USA, 1985)
R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT Press, Cambridge, MA, USA, 1998)
T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15:116–132. (IEEE Press, Piscataway, NJ, USA, 1985)
L.A. Zadeh, Towards a theory of fuzzy systems, in Aspects of Networks and System Theory ed. by R.E. Kalman and N. de Claris, pp. 469–490. (Rinehart and Winston, New York, USA, 1971)
L.A. Zadeh. A rationale for fuzzy control. J. Dyn. Syst. Measure. Control 94(1):3–4. (American Society of Mechanical Engineers (ASME), New York, NY, USA, 1972)
L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3:28–44. (IEEE Press, Piscataway, NJ, USA, 1973)
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Fuzzy Control. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_19
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