The influence of the different distributed phase-randomized on the experimental data obtained in dynamic analysis
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Abstract
In this paper the influence of the differently distributed phase-randomized to the data obtained in dynamic analysis for critical value is studied. The calculation results validate that the sufficient phase-randomized of the different distributed random numbers are less influential on the critical value. This offers the theoretical foundation of the feasibility and practicality of the phase-randomized method.
Key words
experimental data surrogate data critical value phaserandomized random timeseries chaotic timeseriesPreview
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References
- [1]S. Rombouts and R. Keunen, Investigation of nonlinear structure in multichannel EEG,Phys. Lett,A202 (1995), 352–358.CrossRefGoogle Scholar
- [2]James Theiler, Spurious dimension from correlation algorithms applied to limited time series data,Phys. Rev.,A34, (1986), 2427–2432.CrossRefGoogle Scholar
- [3]Ma Junhai, Chen Yushu and Liu Zengrong. The critical value for diagnosis of chaotic nature of the data obtained in dynamic analysis,Applied Mathematics and Mechanics (English Ed.),19, 6 (1998), 513–520.CrossRefGoogle Scholar
- [4]Dean Prichard, The correlation dimension of differenced data,Phys. Lett,A191 (1994), 245–250.CrossRefGoogle Scholar
- [5]Matthew B. Kennel, Method to distinguish possible chaos from colored noise and to determine embedding parameters,Phys. Rev. Lett,A46 (1992 3111–3118.Google Scholar
- [6]P. E. Rapp and A. M. Albano, Filtered noise can mimic low-dimensional chaotic attractors,Phys. Rev.,E47 (1993), 2289–2297.CrossRefGoogle Scholar
- [7]Dean Prichard, Generating surrogate data for timeseries with several simultaneously measured variables,Phys. Rev. Lett,191 (1994), 230–245.Google Scholar
- [8]P. E. Rapp and A. M. Albano, Phase-randomized surrogates can produce spurious identifications of non-random structure,Phys. Lett A192 (1994), 27–33.CrossRefGoogle Scholar
- [9]Henry D. I. Abarbanel, Prediction in chaotic nonlinear systems methods for timeseries with broadband Fourier spectra.,Phys,B5 (1991), 1347–1375.MATHMathSciNetGoogle Scholar
- [10]M. Casdagli and Alistair Mees, Modeling chaotic motions of a string from experimental data,Phys. Rev.,E54 (1992), 303–328.Google Scholar
- [11]P. E. Rapp and A. M. Albano, Predicting chaotic timeseries,Phys. Rev.,E47 (1993), 2289–2297.CrossRefGoogle Scholar
- [12]J. Luis Cabrera and E. Javier, Numerical analysis of transient behavior in the discrete random Logistic equation with delay,Phys. Lett,A197 (1995), 19–24.MathSciNetCrossRefGoogle Scholar
- [13]Eric J. Kostelich, Problems in estimating dynamics from data,Phys.,D58 (1992), 138–152.MathSciNetCrossRefGoogle Scholar
- [14]S. J. Schiff and T. Chang, Information transport in temporal systems,Phys. Rev. Lett., A (1992). 378–393.Google Scholar
- [15]Peter Grassberger, Finite sample corrections to entropy and dimension estimates,Phys. Lett.,A125 (1988), 369–373.MathSciNetCrossRefGoogle Scholar
- [16]James Theiler, Some comments on the correlation dimension of noise,Phys. Lett.,A155 (1991), 480–493.MathSciNetCrossRefGoogle Scholar
- [17]J. Timonen and H. Koskinen, An improved estimator of dimension and some comments on providing confidence intervals,Geophys. Res. Lett.,20 (1993), 1527–1536.Google Scholar
- [18]D. Prichard and C. P. price, Reconstructing attractors from scalar timeseries: a comparison of singular system and redundancy criteria,Geophys. Res.,20 (1993), 2817–2825.Google Scholar
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© Editorial Committee of Applied Mathematics and Mechanics 1998