Abstract:
Periodicities in a dynamical system are determined by using only one variable. Embedding the data in higher dimensional spaces allows us to find recurrence time associated with particular points in the attractor. The present analysis is useful for experimental time series where traditional tools (e.g., Fourier Transform) fail in detecting some intrinsic frequencies of the system. © 1995 Elsevier Science Ltd.
Referencias:
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Citas:
---------- APA ----------
Ortega, G. & Romanelli, L.
(1995)
. Detecting hidden frequencies in dynamical time series: A numerical report. Chaos, Solitons and Fractals, 6(C), 411-415.
http://dx.doi.org/10.1016/0960-0779(95)80048-L---------- CHICAGO ----------
Ortega, G., Romanelli, L.
"Detecting hidden frequencies in dynamical time series: A numerical report"
. Chaos, Solitons and Fractals 6, no. C
(1995) : 411-415.
http://dx.doi.org/10.1016/0960-0779(95)80048-L---------- MLA ----------
Ortega, G., Romanelli, L.
"Detecting hidden frequencies in dynamical time series: A numerical report"
. Chaos, Solitons and Fractals, vol. 6, no. C, 1995, pp. 411-415.
http://dx.doi.org/10.1016/0960-0779(95)80048-L---------- VANCOUVER ----------
Ortega, G., Romanelli, L. Detecting hidden frequencies in dynamical time series: A numerical report. Chaos Solitons Fractals. 1995;6(C):411-415.
http://dx.doi.org/10.1016/0960-0779(95)80048-L