Skip to main content
  • 367 Accesses

Abstract

The following considerations about the identification methods of so-called separable nonlinearities are based on [6]. In this chapter we will present an extension of the identification of isolated nonlinearities. In many applications it is necessary to identify multiple nonlinear influences simultaneously. A method for the identification of separable nonlinearities with guaranteed Ljapunov stability will be explained here.

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 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
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Beuschel M., Hangl F.: Kriterien zur Optimalen Auslegung Neuronaler Netze am Beispiel desGRNN Lehrstuhl für Elektrische Antriebstechnik, TU München, 1997.

    Google Scholar 

  2. Beuschel M., Hangl F.: Online-Invertierung Neuronaler Netze zur Kompensation von Nichtlin-earitäten. Lehrstuhl für Elektrische Antriebstechnik, TU München, 1997.

    Google Scholar 

  3. Brause R.: Neuronale Netze. Teubner Verlag, Stuttgart.

    Google Scholar 

  4. Engell S.: Entwurf nichtlinearer Regelungen. R. Oldenbourg Verlag, München.

    Google Scholar 

  5. Föllinger O.: Regelungstechnik. Hüthig Verlag, 1991.

    Google Scholar 

  6. Hangl F., Lenz U., Schröder D.: Theorie des systematischen Entwurfs lernfähiger Beobachter für eine Klassenichtlinearer Strecken. Proceedings GMA Workshop Interlaken, 1997.

    Google Scholar 

  7. Lenz U.: Theorie des systematischen Entwurfs lernfähiger Beobachter für Streckenmit isolierter Nichtlinearität. Lehrstuhl für Elektrische Antriebstechnik, TU München, 1997.

    Google Scholar 

  8. Narendra K. S.: Stable Adaptive Systems. Prentice Hall, 1989.

    MATH  Google Scholar 

  9. Schaffner C: Anwendung Neuronaler Netze in der Automatisierungstechnik. Lehrstuhl für Elektrische Antriebstechnik, TU München, 1995.

    Google Scholar 

  10. Schröder D.: Elektrische Antriebe 2. Springer-Verlag, Berlin, Heidelberg, 1995.

    Google Scholar 

  11. Specht D. F.: A General Regression Neural Network. IEEE Transactions of Neural Networks, vol. 2, no. 6, 1991.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hangl, F. (2000). Identification of Separable Nonlinearities. In: Schröder, D. (eds) Intelligent Observer and Control Design for Nonlinear Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04117-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-04117-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08346-4

  • Online ISBN: 978-3-662-04117-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics