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

  • Tadeusz Łobos
  • Andrzej Cichocki
  • Paweł Kostyła
  • Zbigniew Wacławek
Part VIII: Implementations
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)


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.


adaptive algorithms parameter estimation neural networks optimization problems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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. 2.
    S. Osowski. Neural Networks for Estimation of Harmonic Components in a Power System. IEE Proc.-C, 139, 129–135, 1992.Google Scholar
  3. 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. 4.
    S.-I. Amari. Mathematical Foundations of Neurocomputing. Proc. IEEE, 78, 1443–1463, 1990.Google Scholar
  5. 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. 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. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Tadeusz Łobos
    • 1
  • Andrzej Cichocki
    • 2
  • Paweł Kostyła
    • 1
  • Zbigniew Wacławek
    • 1
  1. 1.Technical University of WrocławWrocławPoland
  2. 2.FRP Riken - ABS LaboratoryInstitute of Physical and Chemical ResearchJapan

Personalised recommendations