Skip to main content

Fuzzy Inference Modeling Method Based on T-S Fuzzy System

  • Conference paper
  • First Online:
Fuzzy Systems & Operations Research and Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 367))

Abstract

A kind of fuzzy inference modeling method based on T-S fuzzy system is proposed. New input-output models and state-space models are constructed respectively by applying this method to time-invariant second-order freedom movement systems modeling. The obtained differential equation models are used to simulate the second-order equations, and the results show that the models achieve a good approximation precision.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Wang L.X.: A Course in Fuzzy Systems and Control. Tsinghua University Press, Beijing (2003)

    Google Scholar 

  2. Yuan, X.H., Liu, Z.L., Lee, E.S.: Center-of-gravity fuzzy systems based on normal fuzzy implications. Comput. Math. Appl. 61(9), 2879–2898 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  3. Li, Y.M., Shi, Z.K., Li, Z.H.: Approximation theory of fuzzy systems based upon genuine many-valued implications-SISO cases. Fuzzy Sets Syst. 130(2), 147–157 (2002)

    Article  MATH  Google Scholar 

  4. Zeng, X.J., Madan, G.S.: Approximation theory of fuzzy systems-SISO case. IEEE Trans. Fuzzy Syst. 2(2), 162–176 (1994)

    Article  Google Scholar 

  5. Wang, J.Y., Liu, M., Li, H.X.: Analysis of difference between control function and interpolation expression of SISO fuzzy controller. Acta Electronica Sin. 37(2), 424–428 (2009)

    Google Scholar 

  6. Shan, W.W., Jin, D.M., Liang, Y.: Variable universe adaptive fuzzy logic controller CMOS analog chip implementation. Acta Electronica Sin. 37(5), 913–917 (2009)

    Google Scholar 

  7. Yuan, X.H., Li, H.X., Sun, K.B.: Fuzzy Systems and their approximation capability based on parameter singleton fuzzifier methods. Acta Electronica Sin. 39(10), 2372–2377 (2011)

    Google Scholar 

  8. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I. Inform. Sci. 8(3), 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  9. Wang, G.J.: Non-Classical Logic and Approximate Reasoning. Science Press, Beijing (2000)

    Google Scholar 

  10. Li, H.X.: Interpolation mechanism of fuzzy control. Sci. China (Series E) 41(3), 312–320 (1998)

    Article  MATH  Google Scholar 

  11. Li, H.X., Wang, J.Y., Miao, Z.H.: Modeling on fuzzy control systems. Sci. China (Series A) 12(45), 1506–1517 (2002)

    Google Scholar 

  12. Li, H.X., Song, W.Y., Yuan, X.H., Li, Y.C.: Time-varying system modeling method based on fuzzy inference. J. Syst. Sci. Math. Sci. 29(8), 1109–1128 (2009)

    Google Scholar 

  13. Yuan, X.H., Li, H.X., Yang, X.: Fuzzy system and fuzzy inference modeling method based on fuzzy transformation. Acta Electronica Sin. 41(4), 674–680 (2013)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  15. Ban, X.J., Gao, X.Z., Huang, X.L., Vasilakos, A.V.: Stability analysis of the simplest Takagi-sugeno fuzzy control system using circle criterion. Inform. Sci. 177(20), 4387–4409 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Liu, P.Y., Li, H.X.: Hierarchical T-S fuzzy system and its universal approximation. Inform. Sci. 169(3), 279–303 (2005)

    Google Scholar 

  17. Chen, X.J.: Research on TS fuzzy Model Based Simulation of Double Inverted Pendulum System. Hefei University of Technology, Hefei (2005)

    Google Scholar 

Download references

Acknowledgments

Thanks to the support by National Science Foundation of China (No. 90818025 and No. 61074044).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xue-hai Yuan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Jiang, Mz., Zhang, Cl., Yuan, Xh., Li, Hx. (2016). Fuzzy Inference Modeling Method Based on T-S Fuzzy System. In: Cao, BY., Liu, ZL., Zhong, YB., Mi, HH. (eds) Fuzzy Systems & Operations Research and Management. Advances in Intelligent Systems and Computing, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-319-19105-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19105-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19104-1

  • Online ISBN: 978-3-319-19105-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics