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.
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References
T. Łobos. Nonrecursive Methods for Real-Time Determination of Basic Waveforms of Voltages and Currents. IEE Proc.-C, 136, 347–351, 1989.
S. Osowski. Neural Networks for Estimation of Harmonic Components in a Power System. IEE Proc.-C, 139, 129–135, 1992.
G.H. Golub and C.F. Van Loan. An Analysis of the Total Least Squares Problem. SIAM J. Numer Anal, 17, 883–893, 1980.
S.-I. Amari. Mathematical Foundations of Neurocomputing. Proc. IEEE, 78, 1443–1463, 1990.
A. Cichocki and R. Unbehauen. Neural Networks for Optimization and Signal Processing. Chap. 2.5 and 8. Teubner-Wiley. Stuttgart, 1993.
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.
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.
B. Widrow and M. Lehr. 30 years of adaptive neural networks: perceptron, madaline and back propagation, Proc. IEEE, 78, 1415–1442, 1990.
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© 1997 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/BFb0020313
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