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Analysis of Chaotic Data with Mathematica
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We present Mathematica programs to analyze chaotic data. The following topics are considered: - delay time (with the method of average mutual information) - embedding dimension (with the method of false nearest neighbors) - correlation dimension (with the method of correlation exponent) - maximal Lyapunov exponent (with the method of local divergence rates) - nonlinear prediction (with the method of analogues) As examples, we consider data derived from the logistic, Hénon, and Lorenz models as well as real NMR laser data.
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chaos, chaotic data, analysis of chaotic data, chaotic data analysis, nonlinear models, delay time, embedding dimension, correlation dimension, maximal Lyapunov exponent, nonlinear prediction, average mutual information, false nearest neighbors, correlation exponent, local divergence rates, method of analogues, logistic model, Hénon model, Lorenz model, NRM laser
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| i_ Part 1.zip (7.7 MB) - ZIP archive | | i_ Part 2.zip (5.2 MB) - ZIP archive | | i_ Part 3.zip (6.1 MB) - ZIP archive |
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