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

Tadeusz Łobos, Andrzej Cichocki, Paweł Kostyła, Zbigniew Waclawek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings
EditorsWulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud
PublisherSpringer Verlag
Pages1194-1198
Number of pages5
ISBN (Print)3540636315, 9783540636311
DOIs
Publication statusPublished - 1997
Externally publishedYes
Event7th International Conference on Artificial Neural Networks, ICANN 1997 - Lausanne, Switzerland
Duration: 8 Oct 199710 Oct 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1327
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Artificial Neural Networks, ICANN 1997
Country/TerritorySwitzerland
CityLausanne
Period8/10/9710/10/97

Keywords

  • Adaptive algorithms
  • Neural networks
  • Optimization problems
  • Parameter estimation

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