Please use this identifier to cite or link to this item: https://doi.org/10.1063/1.2827500
Title: Model-based detector and extraction of weak signal frequencies from chaotic data
Authors: Zhou, C. 
Cai, T.
Heng Lai, C. 
Wang, X. 
Lai, Y.-C.
Issue Date: 2008
Citation: Zhou, C., Cai, T., Heng Lai, C., Wang, X., Lai, Y.-C. (2008). Model-based detector and extraction of weak signal frequencies from chaotic data. Chaos 18 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1063/1.2827500
Abstract: Detecting a weak signal from chaotic time series is of general interest in science and engineering. In this work we introduce and investigate a signal detection algorithm for which chaos theory, nonlinear dynamical reconstruction techniques, neural networks, and time-frequency analysis are put together in a synergistic manner. By applying the scheme to numerical simulation and different experimental measurement data sets (H́non map, chaotic circuit, and NH3 laser data sets), we demonstrate that weak signals hidden beneath the noise floor can be detected by using a model-based detector. Particularly, the signal frequencies can be extracted accurately in the time-frequency space. By comparing the model-based method with the standard denoising wavelet technique as well as supervised principal components analysis detector, we further show that the nonlinear dynamics and neural network-based approach performs better in extracting frequencies of weak signals hidden in chaotic time series. © 2008 American Institute of Physics.
Source Title: Chaos
URI: http://scholarbank.nus.edu.sg/handle/10635/111436
ISSN: 10541500
DOI: 10.1063/1.2827500
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