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Refined composite variable-step multiscale multimapping dispersion entropy: a nonlinear dynamical index
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DOI：10.1007/s11071-023-09145-8.png)
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Abstract
En 中文
Nonlinear dynamical index can measure the complexity for a single time scale of the series, and when combined with coarse-grained methods, multiple time scales can be obtained to extract more information. In this study, a new coarse-grained method called refined composite variable-step multiscale (RCVM) is proposed, which obtains more subseries by setting different initial points and step lengths and thus extracts more potential information; moreover, in order to get a nonlinear dynamical index value with stronger stability, this study proposes the multimapping dispersion entropy (MDE) by averaging multiple classes of effective mapping approaches on the basis of dispersion entropy; by combining MDE and RCVM processing, RCVM-MDE is proposed to be used as a new nonlinear dynamical index, which can reflect the complexity of the series at multiple scales. The results of the four classes of chaotic simulated signals show that RCVM-MDE is not only able to detect the series nonlinear dynamic changes, but also has a very high stability; the results of three classes of real-world signals demonstrate the differentiability of RCVM-MDE compared to other commonly used entropies, as well as the best classification effect.
Keywords:
Nonlinear dynamical index
Coarse-grained
Refined composite variable-step multiscale
Multimapping dispersion entropy
Signal processing
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W
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