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- W2094350403 abstract "We propose a representation plane constructed from parameters of a multilayer neural network, with the aim of characterizing the dynamical character of a learned time series. We find that fluctuation of this plane reveals distinct features of the time series. Specifically, a periodic representation plane corresponds to a periodic time series, even when contaminated with strong observational noise or dynamical noise. We present a theoretical explanation for how the neural network training algorithm adjusts parameters of this representation plane and thereby encodes the specific characteristics of the underlying system. This ability, which is intrinsic to the architecture of the neural network, can be employed to distinguish the chaotic time series from periodic counterparts. It provides a new path toward identifying the dynamics of pseudoperiodic time series. Furthermore, we extract statistics from the representation plane to quantify its character. We then validate this idea with various numerical data generated by the known periodic and chaotic dynamics and experimentally recorded human electrocardiogram data. • We provide a method for identifying the dynamics of pseudoperiodic time series. • The method can directly distinguish periodic dynamics from the chaotic counterparts. • The method shows great advantage of robustness against strong observational noise. • Both experimental and theoretical evidences give positive support to our method." @default.
- W2094350403 created "2016-06-24" @default.
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- W2094350403 date "2014-02-01" @default.
- W2094350403 modified "2023-09-26" @default.
- W2094350403 title "Response of the parameters of a neural network to pseudoperiodic time series" @default.
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- W2094350403 doi "https://doi.org/10.1016/j.physd.2013.11.002" @default.
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