Skip to main content

Advances in Granular Computing

  • Chapter
  • First Online:
Type-2 Fuzzy Granular Models

Abstract

This book is a research compendium in the area of Fuzzy Granular Computing . Two main branches are handled, a proposed fuzzy granulating algorithm, and higher-type information granuleHigher-type information granule formation, although more work was performed on the latter.

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. Sanchez, M.A., Castillo, O., Castro, J.R., Rodríguez-Díaz, A.: Fuzzy granular gravitational clustering algorithm. North Am. Fuzzy Inf. Process. Soc. 2012, 1–6 (2012)

    Google Scholar 

  2. Sanchez, M.A., Castillo, O., Castro, J.R., Melin, P.: Fuzzy granular gravitational clustering algorithm for multivariate data. Inf. Sci. (Ny) 279, 498–511 (2014)

    Article  MathSciNet  Google Scholar 

  3. Buckley, J.J.: Sugeno type controllers are universal controllers. Fuzzy Sets Syst. 53(3), 299–303 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  4. Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern. SMC-15(1), 116–132 (1985)

    Google Scholar 

  5. Sugeno, M., Kang, G.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994)

    Article  Google Scholar 

  7. Sanchez, M.A., Castro, J.R., Perez-Ornelas, F., Castillo, O.: A hybrid method for IT2 TSK formation based on the principle of justifiable granularity and PSO for spread optimization. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 1268–1273 (2013)

    Google Scholar 

  8. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  9. Jang, J.-S.R.: Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In: Proceedings of the ninth National conference on Artificial intelligence, pp. 762–767 (1991)

    Google Scholar 

  10. Kennedy, J., Eberhart, R.: Particle swarm optimization. In Proceedings of ICNN’95—International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  11. Sanchez, M.A., Castillo, O., Castro, J.R.: Information granule formation via the concept of uncertainty-based information with Interval Type-2 Fuzzy Sets representation and Takagi–Sugeno–Kang consequents optimized with Cuckoo search. Appl. Soft Comput. 27, 602–609 (2015)

    Article  Google Scholar 

  12. Yang, X.-S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214 (2009)

    Google Scholar 

  13. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)

    Article  Google Scholar 

  14. Sanchez, M.A., Castillo, O., Castro, J.R.: Method for measurement of uncertainty applied to the formation of interval type-2 fuzzy sets. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, vol. 601, pp. 13–25. Springer International Publishing, Cham (2015)

    Chapter  Google Scholar 

  15. Sanchez, M.A., Castro, J.R., Castillo, O.: Formation of general type-2 Gaussian membership functions based on the information granule numerical evidence. In: 2013 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA), pp. 1–6 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mauricio A. Sanchez .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 The Author(s)

About this chapter

Cite this chapter

Sanchez, M.A., Castillo, O., Castro, J.R. (2017). Advances in Granular Computing. In: Type-2 Fuzzy Granular Models. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-41288-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41288-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41287-0

  • Online ISBN: 978-3-319-41288-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics