63
ENY-ARTICLE-2008-054
eng
Cichocki, A
FRP Riken - ABS Laboratory, Institute of Physical and Chemical Research, Japan
Adaptive Neural Networks for Robust Estimation of parameters of Noisy Harmonic Signals
1996-07-11
4p
EUSIPCO 1996 VIII. Eight European Signal Processing Conference. Proceedings of Eusipco-96, Trieste, Italy, 10-13 September 1996 / Ed. by G. Ramponi [i in.] Triest : Edizioni LINT, 1996. s. 220-223
In many applications, very fast methods are required for estimating and measurement of parameters of harmonic signals distorted by noise. This follows from the fact that signals have often time varying amplitudes. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper we propose new parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural network principles. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least-squares (LS), the total least-squares (TLS) and the robust TLS criteria are developed and compared. The networks process samples of observed noisy signals and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithm.
neural networks
harmonic signals
TLS
RTLS
Kostyla, P
Wroclaw University of Technology, Poland
Lobos, T
Wroclaw University of Technology
Waclawek, Z
Wroclaw University of Technology
zbigniew.leonowicz@pwr.wroc.pl
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