Automatic Estimation of the Fusion Method Parameters to Reduce Rule Base of Fuzzy Control Complex Systems

  • Yulia Nikolaevna Ledeneva
  • Carlos Alberto Reyes García
  • José Antonio Calderón Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


The application of fuzzy control to large-scale complex systems is not a trivial task. For such systems the number of the fuzzy IF-THEN rules exponentially explodes. If we have l possible linguistic properties for each of n variables, with which we will have l n possible combinations of input values. Large-scale systems require special approaches for modeling and control. In our work the sensory fusion method is studied in an attempt to reduce the size of the inference engine for large-scale systems. This method reduces the number of rules considerably. But, in order to do so, the adequate parameters should be estimated, which, in the traditional way, depends on the experience and knowledge of a skilled operator. In this work, we are proposing a method to automatically estimate the corresponding parameters for the sensory fusion rule base reduction method to be applied to fuzzy control complex systems. In our approach, the parameters of the sensory fusion method are found through the use of genetic algorithms. The implementation process, the simulation experiments, as well as some results are described in the paper.


Fuzzy System Fuzzy Rule Rule Base Fuzzy Control Fuzzy Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Passino, K.M., Yurkovich, S.: Fuzzy control. Addison-Wesley Longman, Amsterdam (1998)Google Scholar
  2. 2.
    Jamshidi, M.: Large-Scale Systems – Modeling, Control and Fuzzy Logic. Prentice Hall Publishing Company, Englewood Cliffs (1996)Google Scholar
  3. 3.
    Jamshidi, M.: Soft Computing. In: Fuzzy Control Systems, pp. 42–56. Springer, Heidelberg (1997)Google Scholar
  4. 4.
    Zadeh, L.A.: Fuzzy sets. Information and Control, 338–353 (1965)Google Scholar
  5. 5.
    Bezdek, J.C.: Cluster validity with fuzzy sets. J. Cybern. 3(3), 58–71 (1974)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Hung, J.Y., et al.: Variable Structure Control: A Survey. IEEE Trans.on Industrial Electronics 40(1), 2–21 (1993)CrossRefGoogle Scholar
  7. 7.
    Zaheeruddin, Anwer, M.J.: A Simple Technique for Generation and Minimization of Fuzzy Rules. In: IEEE International Conference on Fuzzy Systems, May 2005, Memories, Nevada, CD-ROM (2005)Google Scholar
  8. 8.
    Abraham, A.: Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 269–276. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Kasabov, N., Kozma, R., Duch, W.: Rule Extraction from Linguistic Rule Networks and from Fuzzy Neural Networks: Propositional versus Fuzzy Rules. In: Proceedings of the Conference on Neural Networks and Their Applications NEURAP 1998, Marseilles, France, March 1998, pp. 403–406 (1998)Google Scholar
  10. 10.
    Juang, C.-F., Lin, C.-T.: An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications. IEEE Transaction on Fuzzy Systems 6(1), 12–32 (1998)CrossRefGoogle Scholar
  11. 11.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference Systems. IEEE Trans. System Man & Cybernetics 23, 665–685 (1993)CrossRefGoogle Scholar
  12. 12.
    Halgamuge, S.K., Glesner, M.: Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets and Systems 65, 1–12 (1994)CrossRefGoogle Scholar
  13. 13.
    Tschichold-German, N.: RuleNet - A New Knowledge–Based Artificial Neural Network Model with Application Examples in Robotics. PhD thesis, ETH Zerich (1996)Google Scholar
  14. 14.
    Berenji, H.R., Khedkar, P.: Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Networks 3, 724–740 (1992)CrossRefGoogle Scholar
  15. 15.
    Kim, J., Kasabov, N.: Hy FIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks 12, 1301–1319 (1999)CrossRefGoogle Scholar
  16. 16.
    Nauck, D., Kruse, R.: NEFCON-I: An X-Window Based Simulator for Neural Fuzzy Controllers. In: IEEE-ICNN, WCCI 1994, Orlando (1994)Google Scholar
  17. 17.
    Nauck, D., Kruse, R.: NEFCLASS - A Neuro-Fuzzy Approach for the Classification of Data. In: Symposium on Applied Computing, SAC 1995, Nashville (1995)Google Scholar
  18. 18.
    Eshelman, L.J.: The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In: Rawlins, G. (ed.) Foundations of Genetic Algorithms, pp. 265–283. Morgan Kaufmann, San Francisco (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yulia Nikolaevna Ledeneva
    • 1
  • Carlos Alberto Reyes García
    • 1
  • José Antonio Calderón Martínez
    • 2
  1. 1.National Institute of Astrophysics, Optics and Electronics 
  2. 2.Technological Institute of Aguascalientes 

Personalised recommendations