Structure identification of functional-type fuzzy models with application to modelling nonlinear dynamic plants

  • P. Kortmann
  • H. Unbehauen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1226)


A new fuzzy model structure identification method, based on orthogonalisation and statistical tests, as well as information criteria to obtain a minimum rule base and a minimum number of membership functions from input-output data, is proposed. The method is applied to functional-type fuzzy models. The applicability of the proposed method to nonlinear static and dynamic systems is illustrated by examples.


Fuzzy model identification structure selection statistical tests orthogonalisation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Kortmann, M.: Die Identifikation nichtlinearer Ein-und Mehrgrößensysteme auf der Basis nichtlinearer Modellansätze. VDI-Verlag, Düsseldorf 1989.Google Scholar
  2. [2]
    Kortmann, M. and H. Unbehauen: Structure detection in the identification of nonlinear systems. APII Automatique productique informatique industrielle, 22 (1988), 5–25.Google Scholar
  3. [3]
    Unbehauen, H.: Some new trends in identification and modeling of nonlinear dynamical systems. J. of Applied Mathematics and Computation, 78 (1996), 279–297.Google Scholar
  4. [4]
    Sugeno, M. and G. Kang: Structure identification of fuzzy models. Fuzzy Sets and Systems, 28 (1988), 15–33.Google Scholar
  5. [5]
    Takagi, T. and M. Sugeno: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 15 (1985), 116–132.Google Scholar
  6. [6]
    Sugeno, M. and T. Yasukawa: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. on Fuzzy Systems, 1 (1993), 7–31.Google Scholar
  7. [7]
    Filev, D.: Fuzzy modeling of complex systems. Int. J. of Approximate Reasoning, 5 (1991), 281–290.Google Scholar
  8. [8]
    Fischer, M.: Fuzzy-modellbasierte Regelung nichtlinearer Prozesse. Proc. 6. Workshop “Fuzzy Control” des GMA-UA 1.4.2, Dortmund 1996, 29–42.Google Scholar
  9. [9]
    Bezdek, J.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York 1981.Google Scholar
  10. [10]
    Yager, R. and D. Filev: Generation of fuzzy rules by mountain clustering. J. Intelligent and Fuzzy Systems, 2 (1994), 209–219.Google Scholar
  11. [11]
    Chiu, S.: Fuzzy model identification based on cluster estimation. J. Intelligent and Fuzzy Systems, 2 (1994), 267–278.Google Scholar
  12. [12]
    Gustafson, D. and W. Kessel: Fuzzy clustering with a fuzzy covariance matrix. Proc. IEEE-CDC (Conference on Decision and Control) 1978, 761–766.Google Scholar
  13. [13]
    Wang, H., K. Tanaka and M. Griffin: An analytical framework of fuzzy modelling and control of nonlinear systems: Stability and design issues. Proc. of American Control Conference (ACC), Seattle, Washington 1995, 2272–2276.Google Scholar
  14. [14]
    Babuška, R. and H. Verbruggen: Model-based methods for design of fuzzy control systems, Journal A, 36 (1995), 56–61.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • P. Kortmann
    • 1
  • H. Unbehauen
    • 1
  1. 1.Control Engineering Laboratory, Faculty of Electrical EngineeringRuhr-University BochumBochum

Personalised recommendations