Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum

This contribution presents an investigation on noise sensitivity of some of the most disseminated techniques employed to estimate Lyapunov exponents from time series. Since noise contamination is unavoidable in cases of data acquisition, it is important to recognize techniques that could be employed...

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Main Authors: L.F.P. Franca, M.A. Savi
Format: Article
Language:English
Published: Hindawi Limited 2003-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2003/437609
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spelling doaj-b0ed0a49aed94cf8befd624dc837f2332020-11-24T21:06:49ZengHindawi LimitedShock and Vibration1070-96221875-92032003-01-01101375010.1155/2003/437609Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear PendulumL.F.P. Franca0M.A. Savi1Instituto Militar de Engenharia, Department of Mechanical and Materials Engineering, 22.290.270, Rio de Janeiro, RJ, BrazilInstituto Militar de Engenharia, Department of Mechanical and Materials Engineering, 22.290.270, Rio de Janeiro, RJ, BrazilThis contribution presents an investigation on noise sensitivity of some of the most disseminated techniques employed to estimate Lyapunov exponents from time series. Since noise contamination is unavoidable in cases of data acquisition, it is important to recognize techniques that could be employed for a correct identification of chaos. State space reconstruction and the determination of Lyapunov exponents are carried out to investigate the response of a nonlinear pendulum. Signals are generated by numerical integration of the mathematical model, selecting a single variable of the system as a time series. In order to simulate experimental data sets, a random noise is introduced in the signal. Basically, the analyses of periodic and chaotic motions are carried out. Results obtained from mathematical model are compared with the one obtained from time series analysis, evaluating noise sensitivity. This procedure allows the identification of the best techniques to be employed in the analysis of experimental data.http://dx.doi.org/10.1155/2003/437609
collection DOAJ
language English
format Article
sources DOAJ
author L.F.P. Franca
M.A. Savi
spellingShingle L.F.P. Franca
M.A. Savi
Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum
Shock and Vibration
author_facet L.F.P. Franca
M.A. Savi
author_sort L.F.P. Franca
title Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum
title_short Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum
title_full Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum
title_fullStr Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum
title_full_unstemmed Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum
title_sort evaluating noise sensitivity on the time series determination of lyapunov exponents applied to the nonlinear pendulum
publisher Hindawi Limited
series Shock and Vibration
issn 1070-9622
1875-9203
publishDate 2003-01-01
description This contribution presents an investigation on noise sensitivity of some of the most disseminated techniques employed to estimate Lyapunov exponents from time series. Since noise contamination is unavoidable in cases of data acquisition, it is important to recognize techniques that could be employed for a correct identification of chaos. State space reconstruction and the determination of Lyapunov exponents are carried out to investigate the response of a nonlinear pendulum. Signals are generated by numerical integration of the mathematical model, selecting a single variable of the system as a time series. In order to simulate experimental data sets, a random noise is introduced in the signal. Basically, the analyses of periodic and chaotic motions are carried out. Results obtained from mathematical model are compared with the one obtained from time series analysis, evaluating noise sensitivity. This procedure allows the identification of the best techniques to be employed in the analysis of experimental data.
url http://dx.doi.org/10.1155/2003/437609
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AT masavi evaluatingnoisesensitivityonthetimeseriesdeterminationoflyapunovexponentsappliedtothenonlinearpendulum
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