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• W2110176621 abstract "Kolmogorov entropy, actually an entropy rate h, has been introduced in chaos theory to characterize quantitatively the overall temporal organization of a dynamics. Several methods have been devised to turn the mathematical definition into an operational quantity that can be estimated from experimental time series. The method based on recurrence quantitative analysis (RQA) is one of the most successful. Indeed, recurrence plots (RPs) offer a trajectory-centered viewpoint circumventing the need of a complete phase space reconstruction and estimation of the invariant measure. RP-based entropy estimation methods have been developed for either discrete-state or continuous-state systems. They rely on the statistical analysis of the length of diagonal lines in the RP. For continuous-state systems, only a lower bound K 2 can be estimated. The dependence of the estimated quantity on the tunable neighborhood radius ϵ involved in constructing the RP, termed the ϵ-entropy, gives a qualitative information on the regular, chaotic or stochastic nature of the underlying dynamics. Although some caveats have to be raised about its interpretation, Kolmogorov entropy estimated from RPs offers a simple, reliable and quantitative index, all the more if it is supplemented with other characteristics of the dynamics." @default.
• W2110176621 created "2016-06-24" @default.
• W2110176621 creator A5068924391 @default.
• W2110176621 creator A5083411884 @default.
• W2110176621 date "2014-07-08" @default.
• W2110176621 modified "2023-09-25" @default.
• W2110176621 title "Estimating Kolmogorov Entropy from Recurrence Plots" @default.
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• W2110176621 doi "https://doi.org/10.1007/978-3-319-07155-8_2" @default.
• W2110176621 hasPublicationYear "2014" @default.
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