Adaptive neural networks for robust estimation of parameters of noisy harmonic signals

A. Cichocki, P. Kostyla, T. Lobos, Z. Waclawek

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
JournalEuropean Signal Processing Conference
Publication statusPublished - 2015
Externally publishedYes
Event8th European Signal Processing Conference, EUSIPCO 1996 - Trieste, Italy
Duration: 10 Sep 199613 Sep 1996


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