[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Least Mean Square vs. Outer Bounding Ellipsoid Algorithm in Confidence Estimation of the GMDH Neural Networks

  • Conference paper
Adaptive and Natural Computing Algorithms (ICANNGA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4432))

Included in the following conference series:

  • 2012 Accesses

Abstract

The paper deals with the problem of determination of the model uncertainty during the system identification with the application of the Group Method of Data Handling (GMDH) neural network. The main objective is to show how to employ the Least Mean Square (LMS) and the Outer Bounding Ellipsoid (OBE) algorithm to obtain the corresponding model uncertainty.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • 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.

Similar content being viewed by others

References

  1. Delaleau, E., Louis, J.P., Ortega, R.: Modeling and Control of Induction Motors. Int. Journal of Applied Mathematics and Computer Science 11, 105–129 (2001)

    MATH  MathSciNet  Google Scholar 

  2. Etien, E., Cauet, S., Rambault, L., Champenois, G.: Control of an Induction Motor Using Sliding Mode Linearization. Int. Journal of Applied Mathematics and Computer Science 12, 523–531 (2001)

    MathSciNet  Google Scholar 

  3. Gupta, M.M., Liang, J., Homma, N.: Static and Dynamic Neural Networks. John Wiley & Sons, Hoboken (2003)

    Google Scholar 

  4. Ivakhnenko, A.G., Mueller, J.A.: Self-organizing of Nets of Active Neurons. System Analysis Modelling Simulation 20, 93–106 (1996)

    Google Scholar 

  5. Korbicz, J., Kościelny, J.M., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer, Berlin (2004)

    MATH  Google Scholar 

  6. Milanese, M., Norton, J., Piet-Lahanier, H., Walter, E. (eds.): Bounding Approaches to System Identification. Plenum Press, New York (1996)

    MATH  Google Scholar 

  7. Mrugalski, M.: Neural Network Based Modelling of Non-linear Systems in Fault Detection Schemes. Ph.D. Thesis (In Polish), University of Zielona Góra, Zielona Góra (2004)

    Google Scholar 

  8. Witczak, M.: Advances in Model-based Fault Diagnosis with Evolutionary Algorithms and Neural Networks. Int. Journal of Applied Mathematics and Computer Science 16, 85–99 (2006)

    MathSciNet  Google Scholar 

  9. Witczak, M., Korbicz, J., Mrugalski, M., Patton, R.J.: A GMDH neural network based approach to robust fault detection and its application to solve the DAMADICS benchmark problem. Control Engineering Practice 14, 671–683 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Bartlomiej Beliczynski Andrzej Dzielinski Marcin Iwanowski Bernardete Ribeiro

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Mrugalski, M., Korbicz, J. (2007). Least Mean Square vs. Outer Bounding Ellipsoid Algorithm in Confidence Estimation of the GMDH Neural Networks. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71629-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71590-0

  • Online ISBN: 978-3-540-71629-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics