Other Methods for Optimization of Type-2 Fuzzy Systems

  • Oscar Castillo
  • Patricia Melin
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES, volume 1)


In this chapter we describe some other works reported in the literature optimizing type-2 fuzzy systems using different kinds of optimization algorithms (other than GAs, PSO or ACO, which were covered in previous chapters). Most of these works have had relative success according to the different areas of application. In this chapter, we offer a representative and brief review of these types of works to illustrate the advantages of using the corresponding optimization techniques for automating the design process or parameters of type-2 fuzzy systems.


Membership Function Fuzzy Controller Modular Neural Network Hierarchical Genetic Algorithm Fuzzy System Design 
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.


  1. 1.
    R.A. Aliev, W. Pedrycz, B.G. Guirimov, R.R. Aliev, U. Ilhan, M. Babagil, S. Mammadli, Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization. Info. Sci. 181(9), 1591–1608 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    D. Hidalgo, P. Melin, O. Castillo, Type-2 fuzzy inference system optimization based on the uncertainty of membership functions applied to benchmark problems. Lecture Notes in Computer Science, vol. 6438 (2010), pp. 454–464Google Scholar
  3. 3.
    D. Hidalgo, P. Melin, O. Castillo, Optimal design of type-2 fuzzy membership functions using genetic algorithms in a partitioned search space, in Proceedings of the IEEE International Conference on Granular Computing, GrC 2010, San Jose, Aug 2010, pp. 212–216Google Scholar
  4. 4.
    S.M.A. Mohammadi, A.A. Gharaveisi, M. Mashinchi, 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. 610–616Google Scholar
  5. 5.
    D. Hidalgo, P. Melin, G. Licea, O. Castillo, Optimization of type-2 fuzzy integration in modular neural networks using an evolutionary method with applications in multimodal biometry. Lecture Notes in Computer Science, vol. 5845 (2009), pp. 454–465Google Scholar
  6. 6.
    D. Hidalgo, P. Melin, O. Mendoza, Evolutionary optimization of type-2 fuzzy systems based on the level of uncertainty, in Proceedings of the IEEE World Congress on Computational Intelligence, WCCI 2010, Barcelona, July 2010Google Scholar
  7. 7.
    O. Castillo, P. Melin, A. Alanis, O. Montiel, R. Sepulveda, Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms. J. Soft Comput. 15(6), 1145–1160 (2011)CrossRefGoogle Scholar
  8. 8.
    J.C.F. Garcia, An evolutive interval type-2 TSK fuzzy logic system for volatile time series identification, in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 666–671Google Scholar
  9. 9.
    R. Muñoz, O. Castillo, P. Melin, Optimization of fuzzy response integrators in modular neural networks with hierarchical genetic algorithms: the case of face, fingerprint and voice recognition. Stud. Comput. Intell. 257, 111–129 (2009)CrossRefGoogle Scholar
  10. 10.
    F. Menolascina, V. Bevilacqua, M. Zarrilli, G. Mastronardi, Induction of fuzzy rules by means of artificial immune systems in bioinformatics. Stud. Fuzziness Soft Comput. 242, 1–17 (2009)CrossRefGoogle Scholar
  11. 11.
    K.J. Poornaselvan, T. Gireesh Kumar, V.P. Vijayan, Agent based ground flight control using type-2 fuzzy logic and hybrid ant colony optimization to a dynamic environment, in Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology, ICETET 2008, 2008, pp. 343–348Google Scholar
  12. 12.
    O. Castillo, A.I. Martinez, A.C. Martinez, Evolutionary computing for topology optimization of type-2 fuzzy systems. Adv. Soft Comput. 41, 63–75 (2007)CrossRefGoogle Scholar
  13. 13.
    L. Astudillo, P. Melin, O. Castillo, A new optimization method based on a paradigm inspired by nature. Stud. Comput. Intell. 312, 277–283 (2010)CrossRefGoogle Scholar
  14. 14.
    O. Castillo, G. Huesca, F. Valdez, Evolutionary computing for topology optimization of type-2 fuzzy controllers. Stud. Fuzziness Soft Comput. 208, 163–178 (2008)CrossRefGoogle Scholar
  15. 15.
    L. Astudillo, O. Castillo, L.T. Aguilar, R. Martinez, Hybrid control for an autonomous wheeled mobile robot under perturbed torques. Lecture Notes in Computer Science, vol. 4529 (2007), pp. 594–603Google Scholar
  16. 16.
    R. Sepulveda, O. Montiel, O. Castillo, P. Melin, Optimizing the MFs in type-2 fuzzy logic controllers, using the human evolutionary model. Int. Rev. Autom. Control 3(1), 1–10 (2011)Google Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Oscar Castillo
    • 1
  • Patricia Melin
    • 1
  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyChula VistaUSA

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