Skip to main content

Other Methods for Optimization of Type-2 Fuzzy Systems

  • Chapter
  • First Online:
Recent Advances in Interval Type-2 Fuzzy Systems

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL,volume 1))

  • 684 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  MathSciNet  Google Scholar 

  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. 454–464

    Google Scholar 

  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. 212–216

    Google Scholar 

  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. 610–616

    Google Scholar 

  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. 454–465

    Google Scholar 

  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 2010

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 666–671

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 343–348

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 594–603

    Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2012 The Author(s)

About this chapter

Cite this chapter

Castillo, O., Melin, P. (2012). Other Methods for Optimization of Type-2 Fuzzy Systems. In: Recent Advances in Interval Type-2 Fuzzy Systems. SpringerBriefs in Applied Sciences and Technology(), vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28956-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28956-9_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28955-2

  • Online ISBN: 978-3-642-28956-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics