Skip to main content
SpringerLink
Log in

Evolutionary System Identification via Descriptive Takagi Sugeno Fuzzy Systems

  • Conference paper
Advances in Intelligent Data Analysis V (IDA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2810))

Included in the following conference series:

  • 1657 Accesses

Abstract

System identification is used to identify relevant input-output space relations. In this article the relations are used to model a descriptive Takagi-Sugeno fuzzy system. Basic terms of system identification, fuzzy systems and evolutionary computation are briefly reviewed. These concepts are used to present the implementation of an evolutionary algorithm which identifies (sub)optimal descriptive Takagi-Sugeno fuzzy systems according to given data. The proposed evolutionary algorithm is tested on the well known gas furnace data set and results are presented.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holen-Day, San Francisco (1970)

    MATH  Google Scholar 

  2. Cordon, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. In: Advances in Fuzzy Systems – Application and Theory, vol. 19, p. 337. World Scientific Publishing Co. Pte. Ltd, Singapore (2001) ISBN 981-02-4016-3

    Google Scholar 

  3. Cox, M.G.: Algorithms for Spline Curves and Surfaces. NPL Report DITC 166/90, 36 pages (1990)

    Google Scholar 

  4. Farag, W., Tawfik, A.: On Fuzzy Identification and the Gas Furnace Data. In: Proc. of the IASTED Int. Conf. about Intelligent Systems and Control, Honolulu Hawaii, USA (2000)

    Google Scholar 

  5. Gaweda, A.E.: Optimal data-driven rule extraction using adaptive fuzzy-neural models. Dissertation submitted to Faculty of the graduate school of the university of Louisville, pp. 44–49 (1997)

    Google Scholar 

  6. Grauel, A., Mackenberg, H.: Mathematical Analysis of the Sugeno Controller Leading to General Design Rules. Fuzzy Sets and Systems 85, 165–175 (1997)

    Article  MathSciNet  Google Scholar 

  7. Grauel, A., Ludwig, L.: A Construction of Differentiable Membership Functions. Fuzzy Sets and Systems 101, 215–225 (1999)

    MathSciNet  Google Scholar 

  8. Grauel, A.: Handbook of Mathematics Bronstein. In: Semendjajew (ed.) Fuzzy Logic, Musiol and Mühlig. ch. 5.9, pp. 372–391. Springer, Heidelberg (2003)

    Google Scholar 

  9. Gulson, M., Smith, A.E.: A Hierarchical Genetic Algorithm for System Identification and Curve Fitting with a Supercomputer Implementation. In: IMA Volumes in Mathematics and its Applications – Issue on Evolutionary Computation. Springer, Heidelberg (1998)

    Google Scholar 

  10. Jang, J.R., Sun, C.T.: Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Networks 4(1), 156–159 (1993)

    Article  MATH  Google Scholar 

  11. Nelles, O.: Nonlinear System Identification – From Classical Approaches to Neural Networks and Fuzzy Models, pp. 284–285. Springer, Heidelberg (2001) ISBN 3-540-67369-5

    MATH  Google Scholar 

  12. Renners, I., Grauel, A.: Optimizing lattice-based associative memory networks by evolutionary algorithms. Information Sciences 136, 69–84 (2001)

    Article  MATH  Google Scholar 

  13. Renners, I., Grauel, A.: Variable length coding of genotypes using n-ary trees. In: Proc. of the 9th Int. Conference on Soft Computing – Mendel (2003)

    Google Scholar 

  14. Sarle, W.S.: Frequently asked questions (FAQ) of the neural network newsgroup, http://www.faqs.org/faqs/ai-faq/neural-nets/part3/

  15. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15(1), 116–132 (1985)

    MATH  Google Scholar 

  16. Werntges, H.W.: Partitions of unity improve neural function approximators. In: IEEE Int. Conference on Neural Networks (ICNN), vol. 2, pp. 914–918, San Francisco, USA (1993)

    Google Scholar 

  17. Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning Information Sciences – Application to Approximate Reasoning I–III (1975)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Mathematics and, Centre Computational Intelligence & Cognitive Systems, University of Applied Sciences, D-59494, Soest, Germany, Lübecker Ring 2, Soest Campus

    Ingo Renners & Adolf Grauel

Authors
  1. Ingo Renners
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adolf Grauel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

    Michael R. Berthold

  2. Freie Universität Berlin, Garystr. 21, 14195, Berlin, Germany

    Hans-Joachim Lenz

  3. Department of Computer Science, University of Colorado, Boulder, Colorado, USA

    Elizabeth Bradley

  4. Otto-von-Guericke-University of Magdeburg, Germany

    Rudolf Kruse

  5. Department of Knowledge Processing and Language Engineering, University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Christian Borgelt

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Renners, I., Grauel, A. (2003). Evolutionary System Identification via Descriptive Takagi Sugeno Fuzzy Systems. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45231-7_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40813-0

  • Online ISBN: 978-3-540-45231-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation