Automatic Design and Optimization of Fuzzy Inference Systems

  • Ján Vaščák
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)


Fuzzy inference systems have found a very spread application field, especially in areas, which interact with humans. However, they lack any self-learning capabilities for design of their knowledge bases. Beside such means as neural networks and interpolation methods also genetic algorithms are used in this area. First of all the conventional approaches of genetic algorithms have found use in rule-based fuzzy inference systems. In addition, other approaches, as parts of a broader group of evolutionary algorithms, like particle swarm optimization and simulated annealing were applied for this area. Finally, various other promising approaches like fuzzy cognitive maps were adapted for fuzzy logic, too. Therefore, the structure of this chapter has three basic parts and it deals at first with adaptation and knowledge acquisition possibilities of fuzzy inference systems in general. Consecutively, methods of using genetic algorithms for the design of rule-based fuzzy inference systems are described. In the last part the scope of fuzzy cognitive maps is analysed and some adaptation approaches based on evolutionary algorithms are introduced.


Particle Swarm Optimization Fuzzy Logic Fuzzy System Fuzzy Controller Fuzzy Inference System 
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.


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  1. 1.
    Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic and Soft Computing 17(2–3), 255–287 (2011)Google Scholar
  2. 2.
    Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems Applications 36(3), 5221–5229 (2009)CrossRefGoogle Scholar
  3. 3.
    Cara, A.B., Pomares, H., Rojas, I.: A new methodology for the online adaptation of fuzzy self-structuring controllers. IEEE Transactions on Fuzzy Systems 19(3), 449–464 (2001)CrossRefGoogle Scholar
  4. 4.
    Casillas, J., Carse, B., Bull, L.: Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems 15(4), 536–550 (2007)CrossRefGoogle Scholar
  5. 5.
    Chen, S.M.: Cognitive-map-based decision analysis based on NPN logics. Fuzzy Sets and Systems 71(2), 155–163 (1995)CrossRefGoogle Scholar
  6. 6.
    Clerc, M., Kennedy, J.: The particle swarm–explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  7. 7.
    Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: Current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic Fuzzy Systems — Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. series Advances in Fuzzy Systems — Applications and Theory, vol. 19. World Scientific (2001)Google Scholar
  9. 9.
    Damousis, I., Dokopoulos, P.: A fuzzy expert system for the forecasting of wind speed and power generation in wind farms. In: Proc. The 22nd IEEE on Power Industry Computer Applications (PICA), Sydney, Australia, pp. 63–69 (2001)Google Scholar
  10. 10.
    Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control, 2nd edn. Springer (1996)Google Scholar
  11. 11.
    Ghazanfari, M., Alizadeh, S., Fathian, M., Koulouriotis, D.E.: Comparing simulated annealing and genetic algorithm in learning FCM. Applied Mathematics and Computation 192(1), 56–68 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Groumpos, P.P.: Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems. In: Glykas, M. (ed.) Fuzzy Cognitive Maps. STUDFUZZ, vol. 247, pp. 1–22. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  14. 14.
    Ishibuchi, H., Yamamoto, T., Nakashima, T.: Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35(2), 359–365 (2005)CrossRefGoogle Scholar
  15. 15.
    Johanyák, Z.C., Kovács, S.: A brief survey and comparison on various interpolation-based fuzzy reasoning methods. Acta Polytechnica Hungarica 3(1), 91–105 (2006)Google Scholar
  16. 16.
    Kosko, B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24(1), 65–75 (1986)zbMATHCrossRefGoogle Scholar
  17. 17.
    Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior. In: Proc. of the 2001 Congress on Evolutionary Computation, Seoul, vol. 1, pp. 364–371 (2001)Google Scholar
  18. 18.
    Lee, M., Takagi, H.: Integrating design stages of fuzzy systems using genetic algorithms. In: Proc. The Second IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), San Francisco, USA, pp. 613–617 (1993)Google Scholar
  19. 19.
    Lin, C.T., Lee, C.S.G.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall PTR, New Jersey (1996)Google Scholar
  20. 20.
    Mansoori, E.G., Zolghadri, M.J., Katebi, S.D.: SGERD: A steady–state genetic algorithm for extracting fuzzy classification rules from data. IEEE Transactions on Fuzzy Systems 16(4), 1061–1071 (2008)CrossRefGoogle Scholar
  21. 21.
    Oblak, S., Škrjanc, I., Blažič, S.: If approximating nonlinear areas, then consider fuzzy systems. IEEE Potentials 25(6), 18–23 (2006)CrossRefGoogle Scholar
  22. 22.
    Orriols-Puig, A., Casillas, J., Bernadó-Mansilla, E.: Fuzzy-UCS: A Michigan-style learning fuzzy-classifier system for supervised learning. IEEE Transactions on Evolutionary Computation 13(2), 260–283 (2009)CrossRefGoogle Scholar
  23. 23.
    Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.D., Groumpos, P.P., Vrahatis, M.N.: Fuzzy cognitive maps learning using particle swarm optimization. International Journal of Intelligent Information Systems 25(1), 95–121 (2005)CrossRefGoogle Scholar
  24. 24.
    Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal link. Int. Journal of Human–Computer Studies 64(8), 727–743 (2006)CrossRefGoogle Scholar
  25. 25.
    Pozna, C., Troester, F., Precup, R.E., Tar, J.K., Preitl, S.: On the design of an obstacle avoiding trajectory: Method and simulation. Mathematics and Computers in Simulation 79(7), 2211–2226 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Prado, R., García-Galán, S., Muñoz Expósito, J., Yuste, A.: Knowledge acquisition in fuzzy–rule–based systems with particle–swarm optimization. IEEE Transactions on Fuzzy Systems 18(6), 1083–1097 (2010)CrossRefGoogle Scholar
  27. 27.
    Prado, R., García-Galán, S., Yuste, A., Muñoz Expósito, J., Bruque, S.: Genetic fuzzy rule-based meta-scheduler for grid computing. In: 4th Int. Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), Mieres, Spain, pp. 51–56 (2010)Google Scholar
  28. 28.
    Procyk, T., Mamdani, E.: A linguistic self-organizing process controller. Automatica 15, 15–30 (1979)zbMATHCrossRefGoogle Scholar
  29. 29.
    Smith, J.F.: Co–evolving fuzzy decision trees and scenarios. In: IEEE Congress on Evolutionary Computation (CEC), Hong Kong, China, pp. 3167–3176 (2008)Google Scholar
  30. 30.
    Smith, S.: A learning system based on genetic adaptive algorithms. Ph.D. thesis, Department of Computer Science, University of Pittsburgh, USA (1980)Google Scholar
  31. 31.
    Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems 153(3), 371–401 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)zbMATHGoogle Scholar
  33. 33.
    Vaščák, J., Kováčik, P., Hirota, K., Sinčák, P.: Performance-based adaptive fuzzy control of aircrafts. In: Proc. The 10th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), Melbourne, Australia, pp. 761–765 (2001)Google Scholar
  34. 34.
    Vaščák, J., Madarász, L.: Adaptation of fuzzy cognitive maps – a comparison study. Acta Polytechnica Hungarica 7(3), 109–122 (2010)Google Scholar
  35. 35.
    Wagner, C., Hagras, H.: A genetic algorithm based architecture for evolving type–2 fuzzy logic controllers for real world autonomous mobile robots. In: Proc. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), London, United Kingdom, pp. 1–6 (2007)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Technical University of KošiceKošiceSlovakia

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