The proceedings contain 27 papers. The topic discussed include: fast variational algorithm for clutter removal through pyramidal domain decomposition;archetype classification in an iterated transformation image compre...
The proceedings contain 27 papers. The topic discussed include: fast variational algorithm for clutter removal through pyramidal domain decomposition;archetype classification in an iterated transformation image compression algorithm;optical real-time sensor of fractal dimension and engineering applications;parametric entrainment of systems governed by ordinary differential equations;stabilization of chaotic oscillations in dynamical systems: rigorous results;continuous control of chaos by self-controlling feedback: stabilization of unstable periodic and aperiodic orbits;and signal analysis applications of nonlineardynamics and higher-order statistics.
Because of the trajectory instability, time reversal is not possible beyond a certain evolution time and hence the time irreversibility prevails under the finite-accuracy trajectory computation. This therefore provide...
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ISBN:
(纸本)0819412864
Because of the trajectory instability, time reversal is not possible beyond a certain evolution time and hence the time irreversibility prevails under the finite-accuracy trajectory computation. This therefore provides a practical reconciliation of the dynamic reversibility and macroscopic irreversibility (blessing of chaos). On the other hand, the trajectory instability is also responsible for a limited evolution time, so that finite-accuracy computation would yield a pseudo-orbit which is totally unrelated to the true trajectory (curse of chaos). For the inviscid 2D flow, however, we can accurately compute the long- time average of flow quantities with a pseudo-orbit by invoking the ergodic theorem.
The amount of information obtainable from a real dynamical system is limited by the presence of noise, hence noise-reduction techniques are important in all fields in which time-varying signals exist. This paper inves...
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ISBN:
(纸本)0819412864
The amount of information obtainable from a real dynamical system is limited by the presence of noise, hence noise-reduction techniques are important in all fields in which time-varying signals exist. This paper investigates the use of two such techniques in attractor reconstruction and analysis using time series recorded from a real dynamical system, one involving singular value decomposition and the other Neymark decomposition. The latter was found to have a number of advantages over the former: specifically, it permitted a more reliable estimate of effective embedding dimension, and when used in conjunction with the Grassberger-Procaccia algorithm to measure correlation dimension, it permitted more rapid calculation convergence and also seemed less sensitive to any residual noise or saturation in the time series. The application of both methods will be described, and the advantages claimed for the Neymark decomposition technique substantiated using actual experimental data.
The study of nonlineardynamics has been an active area of research since 1960s, after certain path-breaking discoveries, leading to the concepts of solitons, integrability, bifurcations, chaos and spatio-temporal pat...
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The study of nonlineardynamics has been an active area of research since 1960s, after certain path-breaking discoveries, leading to the concepts of solitons, integrability, bifurcations, chaos and spatio-temporal patterns, to name a few. Several new techniques and methods have beer. developed to understand nonlinear systems at different levels. Along with these, a multitude of potential applications of nonlineardynamics have also been enunciated. In spite of these developments, several challenges, some of them fundamental and others on the efficacy of these methods in developing cutting edge technologies, remain to be tackled. In this article, a brief personal perspective of these issues is presented.
The qualitative and quantitative combined nonlineardynamics model proposed in this paper fill the gap in nonlineardynamics model in terms of qualitative and quantitative combined methods, allowing the qualitative mo...
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The qualitative and quantitative combined nonlineardynamics model proposed in this paper fill the gap in nonlineardynamics model in terms of qualitative and quantitative combined methods, allowing the qualitative model and quantitative model to perfectly combine and overcome their weaknesses by learning from each other. These two types of models use their strengths to make up for the other's deficiencies. The qualitative and quantitative combined models can surmount the weakness that the qualitative model cannot be applied and verified in a quantitative manner, and the high costs and long time of multiple construction as well as verification of the quantitative model. The combined model is more practical and efficient, which is of great significance for nonlineardynamics. The qualitative and quantitative combined modeling and model analytical method raised in this paper is not only applied to nonlineardynamics, but can be adopted and drawn on in the modeling and model analysis of other fields. Additionally, the analytical method of qualitative and quantitative combined nonlineardynamics model proposed in this paper can satisfactorily resolve the problems with the price system's existing nonlineardynamics model analytical method. The three-dimensional dynamics model of price, supply-demand ratio and selling rate established in this paper make estimates about the best commodity prices using the model results, thereby providing a theoretical basis for the government's macro-control of price. Meanwhile, this model also offer theoretical guidance to how to enhance people's purchasing power and consumption levels through price regulation and hence to improvepeople's living standards. (C) 2015 Elsevier Ltd. All rights reserved.
The study of nonlineardynamics has been an active area of research since 1960s, after certain path-breaking discoveries, leading to the concepts of solitons, integrability, bifurcations, chaos and spatio-temporal pat...
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The study of nonlineardynamics has been an active area of research since 1960s, after certain path-breaking discoveries, leading to the concepts of solitons, integrability, bifurcations, chaos and spatio-temporal patterns, to name a few. Several new techniques and methods have beer. developed to understand nonlinear systems at different levels. Along with these, a multitude of potential applications of nonlineardynamics have also been enunciated. In spite of these developments, several challenges, some of them fundamental and others on the efficacy of these methods in developing cutting edge technologies, remain to be tackled. In this article, a brief personal perspective of these issues is presented.
The final goal of conducted research is to make a comparison of spline and polynomial approximation methods to reconstruct a priori unknown nonlinear map functions based on observed chaotic time series in the additive...
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The final goal of conducted research is to make a comparison of spline and polynomial approximation methods to reconstruct a priori unknown nonlinear map functions based on observed chaotic time series in the additive noise presence. Logistic and tent map functions are considered in detail as an example.
The final goal of conducted research is to make a comparison of spline and polynomial approximation methods to reconstruct a priori unknown nonlinear map functions based on observed chaotic time series in the additive...
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The final goal of conducted research is to make a comparison of spline and polynomial approximation methods to reconstruct a priori unknown nonlinear map functions based on observed chaotic time series in the additive noise presence. Logistic and tent map functions are considered in detail as an example.
Heart rate (HR) variability has been conventionally analyzed with time and frequency domain methods, which measure the overall magnitude of R-R interval fluctuations around its mean value or the magnitude of fluctuati...
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Heart rate (HR) variability has been conventionally analyzed with time and frequency domain methods, which measure the overall magnitude of R-R interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of FIR dynamics by methods based on chaos theory and nonlinear system theory has gained recent interest. This interest is based on observations suggesting that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way. Furthermore, recent observational studies suggest that some indexes describing nonlinear HR dynamics, such as fractal scaling exponents, may provide more powerful prognostic information than the traditional HR variability indexes. In particular, short-term fractal scaling exponent measured by detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Approximate entropy, a nonlinear index of HR dynamics, which describes the complexity of R-R interval behavior, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indexes, eg, Lyapunov exponent and correlation dimensions, which also give information on the characteristics of FIR dynamics, but their clinical utility is not well established. Although concepts of chaos theory, fractal mathematics, and complexity measures of HR behavior in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for future research to expand our knowledge concerning the behavior of cardiovascular oscillations in normal healthy conditions as well as in disease states.
In this paper, ive study on behavior control of robot using orbits of nonlineardynamics. Behavior generation and control using entrainment and synchronization phenomena in nonlinear dynamical system is discussed. The...
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ISBN:
(纸本)0780365763
In this paper, ive study on behavior control of robot using orbits of nonlineardynamics. Behavior generation and control using entrainment and synchronization phenomena in nonlinear dynamical system is discussed. The behavior of the Arnold equation, which is known to show the chaotic behavior of non-compressive perfect fluid, is analyzed. methods to integrate the dynamics into the information processing system of a robot is to be discussed.
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