Skip to main content
SpringerLink
Log in

Expanded Neural Networks in System Identification

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2773))

Abstract

The neural networks are recognized to possess the fault tolerance and learning capability. The neural networks are also used in the identification of nonlinear systems. However in the system identification it is important to whiten a color noise using the noise model. In this paper we propose an expanded neural network in which a noise model is incorporated into the output layer of the neural network. We have developed the learning algorithm converged more quickly than a classical back-propagation algorithm. The proposed algorithm estimates the parameter of the expanded neural network using the least-squares method, and estimates threshold by the fundamental error back-propagation 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 74.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.00
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. Funahashi, K.: On The Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2, 183–192 (1989)

    Article  Google Scholar 

  2. Chen, S., Billings, S.A., Grant, P.M.: Non-linear system identification using neural networks. INT. J. CONTROL 51(6), 1191–1214 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  3. Yamawaki, S., Fujino, M., Imao, S.: Modeling of dynamic systems by neural networks and characteristic analysis. Transactions of the institute of systems, control and information engineers 6(5), 207–212 (1993) (Japanese)

    Article  Google Scholar 

  4. Yamawaki, S., Fujino, M., Imao, S.: An Approximate Maximum Likelihood Estimation of a Class of Nonlinear Systems using Neural Networks and Noise Models. T. ISCIE 12(4), 203–211 (1999) (Japanese)

    Article  MathSciNet  Google Scholar 

  5. Yamawaki, S., Fujino, M., Imao, S.: The Back Propagation Method Using the Least Mean-Square Method for the Output Recurrent Neural Network. T. ISCIE 12(4), 225–233 (1999) (Japanese)

    Article  MathSciNet  Google Scholar 

  6. Yamawaki, S.: A System Identification Method using Expanded Neural Networks, Knowledge-Based Intelligent Information Engineering System & Allied Technologies. In: KES 2002, pp. 358–363. IOS Press, Amsterdam (2002)

    Google Scholar 

  7. Ljung, L.: System Identification-Theory for the User. Prentice-Hall, Englewood Cliffs (1987)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dep. of Electrical Engineering, School of Science & Engineering, Kinki University, Osaka, 577-8502, Japan

    Shigenobu Yamawaki

  2. Knowledge-Based Intelligent Engineering Systems Centre (KES), University of South Australia, Adelaide, Mawson Lakes, South Australia, 5095

    Lakhmi Jain

Authors
  1. Shigenobu Yamawaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lakhmi Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, United Kingdom

    Vasile Palade

  2. Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, BN2 4GJ, Brighton, United Kingdom

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, 5095, Adelaide, SA, Australia

    Lakhmi Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yamawaki, S., Jain, L. (2003). Expanded Neural Networks in System Identification. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_150

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45224-9_150

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40803-1

  • Online ISBN: 978-3-540-45224-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation