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Evaluation of Systems’ Irregularity and Complexity: Sample Entropy, Its Derivatives, and Their Applications across Scales and Disciplines

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ISBN: 9783038973324 9783038973331 Year: Pages: 264 DOI: 10.3390/books978-3-03897-333-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering
Added to DOAB on : 2018-12-10 11:20:37
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ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms.[enter Summary/Description]Based on information theory, a number of entropy measures have been proposed since the 1990s to assess systems’ irregularity, such as approximate entropy, sample entropy, permutation entropy, intrinsic mode entropy, and dispersion entropy, to cite only a few. Among them, sample entropy has been used in a very large variety of disciplines for both univariate and multivariate data. However, improvements to the sample entropy algorithm are still being proposed because sample entropy is unstable for short time series, may be sensitive to parameter values, and can be too time-consuming for long data. At the same time, it is worth noting that sample entropy does not take into account the multiple temporal scales inherent in complex systems. It is maximized for completely random processes and is used only to quantify the irregularity of signals on a single scale. This is why analyses of irregularity—with sample entropy or its derivatives—at multiple time scales have been proposed to assess systems’ complexity. This Book presents contributions related to new and original research based on the use of sample entropy or its derivatives.

Nonlinear Dynamics and Entropy of Complex Systems with Hidden and Self-excited Attractors

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ISBN: 9783038978985 / 9783038978992 Year: Pages: 290 DOI: 10.3390/books978-3-03897-899-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 10:09:00
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In recent years, entropy has been used as a measure of the degree of chaos in dynamical systems. Thus, it is important to study entropy in nonlinear systems. Moreover, there has been increasing interest in the last few years regarding the novel classification of nonlinear dynamical systems including two kinds of attractors: self-excited attractors and hidden attractors. The localization of self-excited attractors by applying a standard computational procedure is straightforward. In systems with hidden attractors, however, a specific computational procedure must be developed, since equilibrium points do not help in the localization of hidden attractors. Some examples of this kind of system are chaotic dynamical systems with no equilibrium points; with only stable equilibria, curves of equilibria, and surfaces of equilibria; and with non-hyperbolic equilibria. There is evidence that hidden attractors play a vital role in various fields ranging from phase-locked loops, oscillators, describing convective fluid motion, drilling systems, information theory, cryptography, and multilevel DC/DC converters. This Special Issue is a collection of the latest scientific trends on the advanced topics of dynamics, entropy, fractional order calculus, and applications in complex systems with self-excited attractors and hidden attractors.

Keywords

new chaotic system --- multiple attractors --- electronic circuit realization --- S-Box algorithm --- chaotic systems --- circuit design --- parameter estimation --- optimization methods --- Gaussian mixture model --- chaotic system --- empirical mode decomposition --- permutation entropy --- image encryption --- hidden attractors --- fixed point --- stability --- nonlinear transport equation --- stochastic (strong) entropy solution --- uniqueness --- existence --- multiscale multivariate entropy --- multistability --- self-reproducing system --- chaos --- hidden attractor --- self-excited attractor --- fractional order --- spectral entropy --- coexistence --- multistability --- chaotic flow --- hidden attractor --- multistable --- entropy --- core entropy --- Thurston’s algorithm --- Hubbard tree --- external rays --- chaos --- Lyapunov exponents --- multiple-valued --- static memory --- strange attractors --- fractional discrete chaos --- entropy --- projective synchronization --- full state hybrid projective synchronization --- generalized synchronization --- inverse full state hybrid projective synchronization --- inverse generalized synchronization --- multichannel supply chain --- service game --- chaos --- entropy --- BOPS --- Hopf bifurcation --- self-excited attractors --- multistability --- sample entropy --- PRNG --- Non-equilibrium four-dimensional chaotic system --- entropy measure --- adaptive approximator-based control --- neural network --- uncertain dynamics --- synchronization --- fractional-order --- complex-variable chaotic system --- unknown complex parameters --- chaotic map --- fixed point --- chaos --- approximate entropy --- implementation --- hidden attractor --- hyperchaotic system --- multistability --- entropy analysis --- hidden attractor --- complex systems --- fractional-order --- entropy --- chaotic maps --- chaos --- spatial dynamics --- Bogdanov Map --- chaos --- laser --- resonator

Entropy Measures for Data Analysis: Theory, Algorithms and Applications

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ISBN: 9783039280322 / 9783039280339 Year: Pages: 260 DOI: 10.3390/books978-3-03928-033-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2020-01-07 09:21:22
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Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.

Keywords

experiment of design --- empirical mode decomposition --- signal analysis --- similarity indices --- synchronization analysis --- auditory attention --- entropy measure --- linear discriminant analysis (LDA) --- support vector machine (SVM) --- auditory attention classifier --- electroencephalography (EEG) --- vague entropy --- distance induced vague entropy --- distance --- complex fuzzy set --- complex vague soft set --- entropy, entropy visualization --- entropy balance equation --- Shannon-type relations --- multivariate analysis --- machine learning evaluation --- data transformation --- sample entropy --- treadmill walking --- center of pressure displacement --- dual-tasking --- analog circuit --- fault diagnosis --- cross wavelet transform --- Tsallis entropy --- parametric t-distributed stochastic neighbor embedding --- support vector machine --- information transfer --- Chinese stock sectors --- effective transfer entropy --- market crash --- system coupling --- cross-visibility graphs --- image entropy --- geodesic distance --- Dempster-Shafer evidence theory --- uncertainty of basic probability assignment --- belief entropy --- plausibility transformation --- weighted Hartley entropy --- Shannon entropy --- learning --- information --- novelty detection --- non-probabilistic entropy --- learning systems --- permutation entropy --- embedded dimension --- short time records --- signal classification --- relevance analysis --- global optimization --- meta-heuristic --- firefly algorithm --- cross-entropy method --- co-evolution --- symbolic analysis --- ordinal patterns --- Permutation entropy --- conditional entropy of ordinal patterns --- Kolmogorov-Sinai entropy --- algorithmic complexity --- information entropy --- particle size distribution --- selfsimilar measure --- simulation --- data analysis --- entropy --- entropy measures --- automatic learning

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