Bio-inspired Optimization of Interval Type-2 Fuzzy Controllers

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 291)


A review of the optimization methods used in the design of type-2 fuzzy systems, which are relatively novel models of imprecision, has been considered in this paper. The fundamental focus of the work has been based on the basic reasons of the need for optimizing type-2 fuzzy systems for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy systems for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this paper, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy systems. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy systems.


Intelligent Control Type-2 Fuzzy Logic Interval Fuzzy Logic 


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  1. 1.
    Bingül, Z., Karahan, O.: A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. Expert Systems with Applications 38(1), 1017–1031 (2011)CrossRefGoogle Scholar
  2. 2.
    Cao, J., Li, P., Liu, H., Brown, D.: Adaptive fuzzy controller for vehicle active suspensions with particle swarm optimization. In: Proceedings of SPIE-The International Society of Optical Engineering, vol. 7129 (2008)Google Scholar
  3. 3.
    Castillo, O., Huesca, G., Valdez, F.: Evolutionary Computing for Topology Optimization of Type-2 Fuzzy Controllers. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Hybrid Intelligent Systems. STUD FUZZ, vol. 208, pp. 163–178. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Castillo, O., Aguilar, L.T., Cazarez-Castro, N.R., Cardenas, S.: Systematic design of a stable type-2 fuzzy logic controller. Applied Soft Computing Journal 8, 1274–1279 (2008)CrossRefGoogle Scholar
  5. 5.
    Castillo, O., Melin, P., Alanis, A., Montiel, O., Sepulveda, R.: Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms. Journal of Soft Computing 15(6), 1145–1160 (2011)CrossRefGoogle Scholar
  6. 6.
    Castro, J.R., Castillo, O., Melin, P.: An Interval Type-2 Fuzzy Logic Toolbox for Control Applications. In: Proceedings of FUZZ-IEEE 2007, London, pp. 1–6 (2007)Google Scholar
  7. 7.
    Castro, J.R., Castillo, O., Martinez, L.G.: Interval type-2 fuzzy logic toolbox. Engineering Letters 15(1), 14 (2007)Google Scholar
  8. 8.
    Cordon, 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, 5–31 (2004)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Cordon, O., Herrera, F., Villar, P.: Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing. International Journal of Approximate Reasoning 25, 187–215 (2000)MATHCrossRefGoogle Scholar
  10. 10.
    Dereli, T., Baykasoglu, A., Altun, K., Durmusoglu, A., Turksen, I.B.: Industrial applications of type-2 fuzzy sets and systems: A concise review. Computers in Industry 62, 125–137 (2011)CrossRefGoogle Scholar
  11. 11.
    Hagras, H.: Hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Transactions on Fuzzy Systems 12, 524–539 (2004)CrossRefGoogle Scholar
  12. 12.
    Juang, C.-F., Hsu, C.-H.: Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control. IEEE Transactions on Industrial Electronics 56(10), 3931–3940 (2009)CrossRefGoogle Scholar
  13. 13.
    Juang, C.-F., Hsu, C.-H.: Reinforcement interval type-2 fuzzy controller design by online rule generation and Q-value-aided ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B Cybernetics 39(6), 1528–1542 (2009)CrossRefGoogle Scholar
  14. 14.
    Karnik, N.N., Mendel, J.M.: An Introduction to Type-2 Fuzzy Logic Systems, Technical Report, University of Southern California (1998)Google Scholar
  15. 15.
    Martinez, R., Castillo, O., Aguilar, L.T.: Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms. Information Sciences 179(13), 2158–2174 (2009)MATHCrossRefGoogle Scholar
  16. 16.
    Martinez, R., Rodriguez, A., Castillo, O., Aguilar, L.T.: Type-2 fuzzy logic controllers optimization using genetic algorithms and particle swarm optimization. In: Proceedings of the IEEE International Conference on Granular Computing, GrC 2010, pp. 724–727 (2010)Google Scholar
  17. 17.
    Mendel, J.M.: Uncertainty, fuzzy logic, and signal processing. Signal Processing Journal 80, 913–933 (2000)MATHCrossRefGoogle Scholar
  18. 18.
    Mohammadi, S.M.A., Gharaveisi, A.A., Mashinchi, M.: An evolutionary tuning technique for type-2 fuzzy logic controller in a non-linear system under uncertainty. In: Proceedings of the 18th Iranian Conference on Electrical Engineering, ICEE 2010, pp. 610–616 (2010)Google Scholar
  19. 19.
    Oh, S.-K., Jang, H.-J., Pedrycz, W.: A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization. Expert Systems with Applications (2011) (article in press)Google Scholar
  20. 20.
    Sepulveda, R., Castillo, O., Melin, P., Rodriguez-Diaz, A., Montiel, O.: Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Information Sciences 177(10), 2023–2048 (2007)CrossRefGoogle Scholar
  21. 21.
    Wagner, C., Hagras, H.: A genetic algorithm based architecture for evolving type-2 fuzzy logic controllers for real world autonomous mobile robots. In: Proceedings of the IEEE Conference on Fuzzy Systems, London (2007)Google Scholar
  22. 22.
    Wu, D., Tan, W.-W.: Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engineering Applications of Artificial Intelligence 19(8), 829–841 (2006)CrossRefGoogle Scholar
  23. 23.
    Yager, R.R.: Fuzzy subsets of type II in decisions. J. Cybernetics 10, 137–159 (1980)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Information Sciences 8, 43–80 (1975)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico

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