Neural networks for automatic fuzzy control system design

  • Jesús Villadangos
  • J. R. González de Mendívil
  • C. F. Alastruey
  • J. R. Garitagoitia
Neural Networks for Communications and Control
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)


In this paper, a method for the automatic design of Fuzzy Control Systems is introduced. The method is based on the identification of the inverse model of the process to be controlled by using a Neural Network. The Neural Network which models the inverse process, is used again to obtain a set of tuples representing the fuzzy variables of the fuzzy controller. In order to obtain the fuzzy linguistic variables involved in the fuzzy controller, a Neural Network is used with the DCL algorithm. Finally, the fuzzy controller is implemented by a decision table. The method has been applied to the automatic development of a fuzzy controller for a highly non linear process.


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  1. [1]
    Faßmer, J. “Adaptive Generierung der Produktionsregeln eines Fuzzy-Regler mit Hilfe eines Neuronalen Netzwerkes”. Diplomarbeit IFW Hannover, December 1992.Google Scholar
  2. [2]
    Isermann, R. “Prozeidentification”. Band 1, 2. Springer Verlag 1988.Google Scholar
  3. [3]
    Kosko. B. “Neural networks and fuzzy systems”. Prentice-Hall International 1992.Google Scholar
  4. [4]
    Lee, C.C. “Fuzzy logic in control systems: Fuzzy logic controler part I-II”. IEEE Transactions on Systems, Man and Cybernetics, Mar/April 1990, vol. 20, n. 2, pp. 1320–1336.Google Scholar
  5. [5]
    Narendra, K.S.; Parthasarathy, K. “Identification and control of dynamical system using neural networks”. IEEE Transactions on Neural Networks, March 1990, vol. 1, n. 1, pp. 4–27.Google Scholar
  6. [6]
    Procyk, T.J.; Mamdami,E.H. “Alinguistic self-organizing process controller”. Automatica 1979,vol. 15, pp. 15–30.Google Scholar
  7. [7]
    Quin, S.-Z.; Su, H.T.; Mc Avoy, T.J. “Comparation of four neural net learning methods for dynamic system identification”. IEEE Transactions on Neural Networks, Jan 92, vol. 3, n. 1, pp. 122–130.Google Scholar
  8. [8]
    Reynold Chu, S.; Shoureshi, R.; Tenorio, M. “Neural networks for system identification”. IEEE Control System Magazine, April 1990, pp. 31–34.Google Scholar
  9. [9]
    Takagi, H. “Fusion technology of fuzzy theory and neural networks suvey and future directions”. Proceedings of the International Conference on Fuzzy Logic and Neural Networks., July 1990, pp. 13–26.Google Scholar
  10. [10]
    Takagi, H.; Hayashi, I. “Fuzzy reasoning”. International Journal of Approximate Reasoning, Mai 1991, vol. 5, n. 5, pp. 191–212.Google Scholar
  11. [11]
    Wang, L-X.; Mendel, J.M. “Generating fuzzy rules by learning from examples”. Proceedings of the 1991 IEEE International Symposium on Intelligent Control. Arlinton, California 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Jesús Villadangos
    • 1
  • J. R. González de Mendívil
    • 2
  • C. F. Alastruey
    • 1
  • J. R. Garitagoitia
    • 2
  1. 1.Dpt of Electricity and ElectronicsUniversity of the Basque CountryBilbaoSpain
  2. 2.Dpt of Automatica, Electronics and System EngineeringPublic University of NavarraPamplonaSpain

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