Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7371
Title: Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals
Authors: Übeyli, Elif Derya
Keywords: recurrent neural network
Doppler ultrasound signals
Chaotic signal
Lyapunov exponents
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: The implementation of recurrent neural networks (RNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the RNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the RNNs trained on these features achieved high classification accuracies. (c) 2007 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2007.04.002
https://hdl.handle.net/20.500.11851/7371
ISSN: 0957-4174
1873-6793
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

10
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

7
checked on Sep 24, 2022

Page view(s)

4
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.