Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Neural Networks

ICANN 1997: Artificial Neural Networks — ICANN'97 pp 1193–1198Cite as

Adaptive on-line learning algorithm for robust estimation of parameters of noisy sinusoidal signals

  • Part VIII: Implementations
  • Conference paper
  • First Online:

  • 82 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

Abstract

In many applications, very fast methods are required for estimating of parameters of harmonic signals distorted by noise. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper new parallel algorithms are proposed, which can be implemented by analogue adaptive circuits employing some neural networks principles. Algorithms based on the least-squares (LS) and the total least-squares (TLS) criteria are developed and compared. Extensive computer simulations confirm the validity and performance of the proposed algorithms.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. T. Łobos. Nonrecursive Methods for Real-Time Determination of Basic Waveforms of Voltages and Currents. IEE Proc.-C, 136, 347–351, 1989.

    Google Scholar 

  2. S. Osowski. Neural Networks for Estimation of Harmonic Components in a Power System. IEE Proc.-C, 139, 129–135, 1992.

    Google Scholar 

  3. G.H. Golub and C.F. Van Loan. An Analysis of the Total Least Squares Problem. SIAM J. Numer Anal, 17, 883–893, 1980.

    Google Scholar 

  4. S.-I. Amari. Mathematical Foundations of Neurocomputing. Proc. IEEE, 78, 1443–1463, 1990.

    Google Scholar 

  5. A. Cichocki and R. Unbehauen. Neural Networks for Optimization and Signal Processing. Chap. 2.5 and 8. Teubner-Wiley. Stuttgart, 1993.

    Google Scholar 

  6. A. Cichocki and T. Lobos. Artificial Neural Networks for Real-Time Estimation of Basic Waveforms of Voltages and Currents. IEEE Trans. on Power Systems, 9, 612–618, 1994.

    Google Scholar 

  7. D.W. Tank and J. Hopfield. Simple Neural Optimization Networks: an A/D Converter, Signal Decision Circuit and a Linear Programming Circuit, IEEE Transactions on Circuits and Systems, 33, 533–541, 1986.

    Google Scholar 

  8. B. Widrow and M. Lehr. 30 years of adaptive neural networks: perceptron, madaline and back propagation, Proc. IEEE, 78, 1415–1442, 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technical University of Wrocław, pl. Grunwaldzki 13, 50-370, Wrocław, Poland

    Tadeusz Łobos, Paweł Kostyła & Zbigniew Wacławek

  2. FRP Riken - ABS Laboratory, Institute of Physical and Chemical Research, Japan

    Andrzej Cichocki

Authors
  1. Tadeusz Łobos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Cichocki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paweł Kostyła
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zbigniew Wacławek
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Łobos, T., Cichocki, A., Kostyła, P., Wacławek, Z. (1997). Adaptive on-line learning algorithm for robust estimation of parameters of noisy sinusoidal signals. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020313

Download citation

  • DOI: https://doi.org/10.1007/BFb0020313

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation