منابع مشابه
Distinguishing noise from chaos.
Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity ...
متن کاملBeyond Benford's Law: Distinguishing Noise from Chaos
Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford's law is designed in order to distinguish noise from chaos by only information from the first digit of considered se...
متن کاملDistinguishing chaos from noise by scale-dependent Lyapunov exponent.
Time series from complex systems with interacting nonlinear and stochastic subsystems and hierarchical regulations are often multiscaled. In devising measures characterizing such complex time series, it is most desirable to incorporate explicitly the concept of scale in the measures. While excellent scale-dependent measures such as epsilon entropy and the finite size Lyapunov exponent (FSLE) ha...
متن کاملDistinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph
A recently proposed methodology called the Horizontal Visibility Graph (HVG) [Luque et al., Phys. Rev. E., 80, 046103 (2009)] that constitutes a geometrical simplification of the well known Visibility Graph algorithm [Lacasa et al., Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to study the distinction between deterministic and stochastic components in time series [L. Lacasa and R. T...
متن کاملRemoving the Noise from Chaos Plus Noise
The problem of extracting a “signal” xn generated by a dynamical system from a times series yn = xn + en, where en is an observational error, is considered. It is shown that consistent signal extraction is impossible when the errors are distributed according to a density with unbounded support and the underlying dynamical system admits homoclinic pairs. It is also shown that consistent signal e...
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ژورنال
عنوان ژورنال: Physical Review Letters
سال: 2007
ISSN: 0031-9007,1079-7114
DOI: 10.1103/physrevlett.99.154102