Skip to main content
SpringerLink
Log in

The Takagi-Sugeno Fuzzy Model Identification Method of Parameter Varying Systems

  • Conference paper
  • First Online:
Book cover Rough Sets and Current Trends in Computing (RSCTC 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1424))

Included in the following conference series:

Abstract

This paper presents the TS model identification method by which a great number of systems whose parameters vary dramatically with working states can be identified via Fuzzy Neural Networks (FNN). The suggested method could overcome the drawbacks of traditional linear system identification methods which are only effective under certain narrow working states and provide global dynamic description based on which further control of such systems may be carried out. Simulation results of a second-order parameter varying system demonstrate the effectiveness of the method.

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

Access this chapter

Log in via an institution
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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, 1992

    Google Scholar 

  2. T. Takagi and M. Sugeno, “Fuzzy identification of system and its applications to modeling and control”, IEEE Trans. on System Man and Cybernetics, vol. SMC-15, no. 1, 1985, pp. 116–132.

    Google Scholar 

  3. M. Sugeno and G. T. Kang, “Structure identification of fuzzy model”, Fuzzy Sets and Systems, vol. 28, 1988, pp. 15–33.

    Article  MATH  MathSciNet  Google Scholar 

  4. M. Sugeno and K. Tanaka, “Successive identification of a fuzzy model and its applications to prediction of a complex system”, Fuzzy Sets and Systems, vol. 42, 1992, pp. 315–334.

    Article  MathSciNet  Google Scholar 

  5. Xie Keming and Zhang Jianwei. “A linear fuzzy model identification method based on fuzzy neural networks”, in Proceedings of the 2nd Worldwide Chinese Intelligence Control and Intelligence Automation Conference, 1997.

    Google Scholar 

  6. Xie Keming and Zhang Jianwei, “An Adaptive Backpropagation Algorithm Based on Error Rate of Change”, submitted to Journal of Taiyuan University of Technology.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, Taiyuan University of Technology, Taiyuan, 030024, P. R. China

    Xie Keming & Zhang Jianwei

  2. Department of Mathematics and Computer Science, San Jose State University, San Jose, CA, 95192, USA

    T. Y. Lin

Authors
  1. Xie Keming
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Y. Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhang Jianwei
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University of Technology, Pl. Politechniki 1, 00-665, Warszawa, Poland

    Lech Polkowski

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warszawa, Poland

    Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Keming, X., Lin, T.Y., Jianwei, Z. (1998). The Takagi-Sugeno Fuzzy Model Identification Method of Parameter Varying Systems. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_22

Download citation

  • DOI: https://doi.org/10.1007/3-540-69115-4_22

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64655-6

  • Online ISBN: 978-3-540-69115-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation