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T–S Fuzzy System Identification Using I/O Data

  • Ruiyun QiEmail author
  • Gang Tao
  • Bin Jiang
Chapter
Part of the Communications and Control Engineering book series (CCE)

Abstract

In this chapter, we consider the identification of T–S fuzzy models based on input–output (I/O) data. The identification of T–S fuzzy models includes two major tasks: structure identification and parameter identification. Structure identification determines the premise (input) variables, the number of fuzzy rules, and the initial positions of membership functions. Parameter identification determines a feasible set of parameters including antecedent (membership function) parameters and consequent parameters under a given structure.

References

  1. Abonyi J (2003) Fuzzy model identification for control. Birkhäuser, BostonCrossRefGoogle Scholar
  2. Angelov PP (2002) Evolving rule-based models: a tool for design of flexible adaptive systems. Springer, HeidelbergCrossRefGoogle Scholar
  3. Angelov PP, Filev DP (2004) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern - Part B: Cybern 34(1):484–498CrossRefGoogle Scholar
  4. Babuska B (1998) Fuzzy modeling for control. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  5. Barada S, Singh H (1998) Generating optimal adaptive fuzzy-neural models of dynamical systems with applications to control. IEEE Trans Syst Man Cybern - Part C: Appl Revi 28(3):297–313CrossRefGoogle Scholar
  6. Bezdek J (1976) A physical interpretation of fuzzy isodata. IEEE Trans Syst Man and Cybern 387–389Google Scholar
  7. Bezdek J (1981) Pattern recognition with fuzzy objective function. Plenum Press, New YorkCrossRefGoogle Scholar
  8. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278Google Scholar
  9. Espinosa J, Vandewalle J, Wertz V (2005) Fuzzy logic, identification and predictive control. Springer, LondonCrossRefGoogle Scholar
  10. Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Mach Intell 7:773–781CrossRefGoogle Scholar
  11. Gustafson DE, Kessel WC (1979) Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of IEEE CDC, San Diego, CA, pp 761–766Google Scholar
  12. Lilly JH (2010) Fuzzy control and identification. Wiley, HobokenCrossRefGoogle Scholar
  13. Ljung L (1987) System identification: theory for the user. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  14. Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE TransNeural Netw 1:4–27CrossRefGoogle Scholar
  15. Nelles O (2001) Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, BerlinCrossRefGoogle Scholar
  16. Oviedo JJE, Vandewalle JPL, Wertz V (2005) Fuzzy logic, identification and predictive control. Springer, LondonzbMATHGoogle Scholar
  17. Sugeno M, Yasukawa T (1993) A Fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1:7–31CrossRefGoogle Scholar
  18. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Engineering and Applied ScienceUniversity of VirginiaCharlottesvilleUSA

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