The influence of the different distributed phase-randomized on the experimental data obtained in dynamic analysis

Applied Mathematics and Mechanics (English Edition)

Volumn 19, Issue 11, 1998, Pages 1033-1042

  Junhai, Ma ^a   Yushu, Chen ^b   Zengrong, Liu ^c  

^a SOUTHEAST UNIVERSITY   (China)

^b TIANJIN UNIVERSITY   (China)

^c SHANGHAI UNIVERSITY   (China)

Author keywords

Chaotic timeseries; Critical value; Experimental data; Phase randomized; Random timeseries; Surrogate data

Indexed keywords

CHAOS THEORY; DATA PROCESSING; MEASUREMENT ERRORS; RANDOM PROCESSES;

EXPERIMENTAL DATA; NONLINEAR CHAOTIC TIME SERIES; PHASE RANDOMIZED METHOD; RANDOM PROCESS TIME SERIES; SURROGATE DATA;

TIME SERIES ANALYSIS;

EID: 0032267343     PISSN: 02534827     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02459191     Document Type: Article

References (18)

1. S. Rombouts and R. Kennen, Investigation of nonlinear structure in multichannel EEG, Phys. Lett, A202 (1995). 352-358.
2. James Theiler, Spurious dimension from correlation algorithms applied to limited time series data, Phys. Rev., A34 (1986), 2427-2432.
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.
4. Dean Prichard. The correlation dimension of differenced data, Phys. Lett, A191 (1994), 245-250.
5. Matthew B. Kennel, Method to distinguish possible chaos from colored noise and to determine embedding parameters, Phys. Rev. Lett, A46 (1992), 3111-3118.
6. P. E. Rapp and A. M. Albano. Filtered noise can mimic low-dimensional chaotic attractors, Phys. Rev., E47 (1993), 2289-2297.
7. Dean Prichard, Generating surrogate data for timeseries with several simultaneously measured variables, Phys. Rev. Lett., 191 (1994), 230-245.
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.
9. Henry D. I. Abarbanel, Prediction in chaotic nonlinear systems methods for timeseries with broadband Fourier spectra, Phys., B5 (1991), 1347-1375.
10. M. Casdagli and Alistair Mees, Modeling chaotic motions of a string from experimental data. Phys. Rev., E54 (1992), 303-328.
11. P. E. Rapp and A. M. Albano, Predicting chaotic timeseries, Phys. Rev., E47 (1993), 2289-2297.
12. J. Luis Cabrera and F. Javier, Numerical analysis of transient behavior in the discrete random Logistic equation with delay, Phys. Lett., A197 (1995), 19-24.
13. Eric J. Kostelich, Problems in estimating dynamics from data, Phys., D58 (1992), 138-152.
14. S. J. Schiff and T. Chang, Information transport in temporal systems, Phys. Rev. Lett., A (1992), 378-393.
15. Peter Grassberger, Finite sample corrections to entropy and dimension estimates, Phys. Lett., A125 (1988), 369-373.
16. James Theiler, Some comments on the correlation dimension of noise, Phys. Lett., A155 (1991), 480-493.
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.
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.