Fuzzy permutation entropy derived from a novel distance between segments of time series

Nonlinear techniques have found an increasing interest in the dynamical analysis of various kinds of systems. Among these techniques, entropy-based metrics have emerged as practical alternatives to classical techniques due to their wide applicability in different scenarios, specially to short and noisy processes. Issued from information theory, entropy approaches are of great interest to evaluate the degree of irregularity and complexity of physical, physiological, social, and econometric systems. Based on Shannon entropy and conditional entropy (CE), various techniques have been proposed; among them, approximate entropy, sample entropy, fuzzy entropy, distribution entropy, permutation entropy, and dispersion entropy are probably the most well-known. After a presentation of the basic information-theoretic functionals, these measures are detailed, together with recent proposals inspired by nearest neighbors and parametric approaches. Moreover, the role of dimension, data length, and parameters in using these measures

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“…181 Zhang et al proposed the fuzzy permutation entropy which combines multiscale entropy, permutation entropy, and fuzzy entropy. 182…”

Section: Ws-rv961x669mentioning

confidence: 99%

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Azami

Faes

Escudero

et al. 2022

Contemporary Mathematics and Its Applications: Monographs, Expositions and Lecture Notes

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“…An improved multi-scale fuzzy entropy method [ 5 ] was developed to obtain information about the operation of a centrifugal compressor using empirical mode decomposition. A finite sequence distance was defined based on inversion, and a new entropy method was derived called fuzzy permutation entropy (FPE) [ 6 ]. The results indicated that FPE could effectively distinguish deterministic signals from random signals.…”

Section: Introductionmentioning

confidence: 99%

Dynamic System Stability Modeling Approach with Sparrow-Inspired Meta-Heuristic Optimization Algorithm

Xia,

Zhang,

Wang

2023

Biomimetics

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Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is employed for flow field feature extraction and modeling object acquisition. The nonlinear characteristic presented in the system is addressed using fuzzy entropy as the identification strategy to provide a basis for instability determination. Using Sparrow Search Algorithm (SSA) optimization, a Radial Basis Function Neural Network (RBFNN) is achieved for the performance prediction of system status. A Logistic SSA solution is first established to seek the optimal parameters of the RBFNN to enhance prediction accuracy and stability. On the basis of the RBFNN-LSSA hybrid model, the stall inception is detected about 35.8 revolutions in advance using fuzzy entropy identification. To further improve the multi-step network model, a Tent SSA is introduced to promote the accuracy and robustness of the model. A wider range of potential solutions within the TSSA are explored by incorporating the Tent mapping function. The TSSA-based optimization method proves a suitable adaptation for complex nonlinear dynamic modeling. And this method demonstrates superior performance, achieving 42 revolutions of advance warning with multi-step prediction. This RBFNN-TSSA model represents a novel and promising approach to the application of system modeling. These findings contribute to enhancing the abnormal warning capability of dynamic systems in compressors.

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“…For a time series, Equation ( 1 ) is valid under the assumption of stationarity despite whether X is univariate or multivariate. Interested readers can refer to other entropies based on Equation ( 1 ) for univariate cases, such as sample entropy [ 12 , 13 ], multiscale entropy [ 14 , 15 ], permutation entropy (PE) [ 16 , 17 , 18 ], etc. These methods have one thing in common—the observed time series is embedded into a phase space in order to reflect the autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

Distinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy

Zhang

Chen

et al. 2022

Entropy

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As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the LSEFSTE100 and LSES&P500 are higher than LSESZI, which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy.

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Fuzzy permutation entropy derived from a novel distance between segments of time series

Cited by 8 publications

References 18 publications

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Dynamic System Stability Modeling Approach with Sparrow-Inspired Meta-Heuristic Optimization Algorithm

Distinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy

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