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FILTERED NOISE CAN MIMIC LOW-DIMENSIONAL CHAOTIC ATTRACTORS
Journal article   Open access

FILTERED NOISE CAN MIMIC LOW-DIMENSIONAL CHAOTIC ATTRACTORS

P E Rapp, A M Albano, T I Schmah and L A Farwell
Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics, v 47(4), pp 2289-2297
01 Apr 1993
PMID: 9960256
url
https://repository.brynmawr.edu/cgi/viewcontent.cgi?article=1005&context=physics_pubsView

Abstract

Physical Sciences Physics Physics, Fluids & Plasmas Physics, Mathematical Science & Technology
This contribution presents four results. First, calculations indicate that when examined by the Grassberger-Procaccia algorithm alone, filtered noise can mimic low-dimensional chaotic attractors. Given the ubiquity Of signal filtering in experimental investigations, this is potentially important. Second, a criterion is derived which provides an estimate of the minimum data accuracy needed to resolve the dimension of an attractor. Third, it is shown that a criterion derived by Eckmann and Ruelle [Physica D 56, 185 (1992)] to estimate the minimum number of data points required in a Grassberger-Procaccia calculation can be used to provide a further check on these dimension estimates. Fourth, it is shown that surrogate data techniques recently published by Theiler and his colleagues [in Nonlinear Modeling and Forecasting, edited by M. Casdagli and S. Eubanks (Addison Wesley, Reading, MA, 1992)] can successfully distinguish between linearly correlated noise and nonlinear structure. These results, and most particularly the first, indicate that Grassberger-Procaccia results must be interpreted with far greater circumspection than has previously been the case, and that the algorithm should be used in combination with additional procedures such as calculations with surrogate data. When filtered signals are examined by this algorithm alone, a finite noninteger value of D2 is consistent with low-dimensional chaotic behavior, but it is certainly not a definitive diagnostic of chaos.

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Collaboration types
Domestic collaboration
Web of Science research areas
Physics, Fluids & Plasmas
Physics, Mathematical
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