Inability of Lyapunov exponents to predict epileptic seizures

It has been claimed that Lyapunov exponents computed from electroencephalogram or electrocorticogram (ECoG) time series are useful for early prediction of epileptic seizures. We show, by utilizing a paradigmatic chaotic system, that there are two major obstacles that can fundamentally hinder the predictive power of Lyapunov exponents computed from time series: finite-time statistical fluctuations and noise. A case study with an ECoG signal recorded from a patient with epilepsy is presented.     

皮层脑电时间序列的相空间重构及非线性特征量的提取

对麻醉的SD大鼠在癫痫发作前后两种状态的皮层脑电(ECoG)的时间序列,用多种有效的方法和分析技术,使得大量的ECoG时间序列得以正确的分析,并得出重要的结论.首先利用延时坐标法重构吸引子;计算互信息函数,取互信息函数第一次达到最小值的延时为重构延时时间,提出将伪邻点法和Cao法相结合的方法确定最佳嵌入维数.然后采用非线性预报和替代数据法相结合的方法确定ECoG为混沌时间序列,从不同角度得出了ECoG不是低维混沌的结论.在ECoG相空间重构的基础上,计算了最大Lyapunov指数(LLE).应用了近似熵这一标量对ECoG进行刻画,计算结果表明:癫痫发作前的皮层脑电的最大Lyapunov指数和近似熵都明显地高于癫痫发作后的,这可能为理解癫痫发病机理,预报癫痫发作和治疗提供一定的思路. 关键词： 皮层脑电 互信息 伪邻点法 最大Lyapunov指数 近似熵     

The largest Lyapunov exponent of chaotic dynamical system in scale space and its application

The largest Lyapunov exponent is an important invariant of detecting and characterizing chaos produced from a dynamical system. We have found analytically that the largest Lyapunov exponent of the small-scale wavelet transform modulus of a dynamical system is the same as the system's largest Lyapunov exponent, both discrete map and continuous chaotic attractor with one or two positive Lyapunov exponents. This property has been used to estimate the largest Lyapunov exponent of chaotic time series with several kinds of strong additive noise.     

Characteristic distributions of finite-time Lyapunov exponents

We study the probability densities of finite-time or local Lyapunov exponents in low-dimensional chaotic systems. While the multifractal formalism describes how these densities behave in the asymptotic or long-time limit, there are significant finite-size corrections, which are coordinate dependent. Depending on the nature of the dynamical state, the distribution of local Lyapunov exponents has a characteristic shape. For intermittent dynamics, and at crises, dynamical correlations lead to distributions with stretched exponential tails, while for fully developed chaos the probability density has a cusp. Exact results are presented for the logistic map, x-->4x(1-x). At intermittency the density is markedly asymmetric, while for "typical" chaos, it is known that the central limit theorem obtains and a Gaussian density results. Local analysis provides information on the variation of predictability on dynamical attractors. These densities, which are used to characterize the nonuniform spatial organization on chaotic attractors, are robust to noise and can, therefore, be measured from experimental data.     

Spatiotemporal chaos and noise

Low-dimensional chaotic dynamical systems can exhibit many characteristic properties of stochastic systems, such as broad Fourier spectra. They are distinguishable from stochastic processes through finite values for their dimension, Lyapunov exponents, and Kolmogorov-Sinai entropy. We discuss how these characteristic observables are modified in spatiotemporal chaotic systems like. coupled map lattices. We analyze with the help of Lyapunov concepts how the stochastic limit is approached and how these properties can be observed directly through local dimension measurements from reconstructed time series. Finally, we discuss the interaction of spatiotemporal attractors with external noise and possible connections to problems of pattern selection and stability.     

自调频混沌系统及其调频码耦合同步

提出了一个闭环自调频混沌系统, 对压控振荡器输出进行采样, 采样值经非线性变换后产生控制压控振荡器下一时刻频率的调频码. 利用一维分岔图、二维分岔图和Lyapunov指数等研究了该系统的动力学行为; 并利用峰值均值功率比、频谱以及输出信号的相关函数等分析了该系统的应用特性. 研究表明, 该系统有复杂的动力行为, 展现分岔、多稳态和混沌等现象, 产生的信号具有低峰值均值功率比和宽频带的特性. 同时还讨论了利用调频码实现系统同步的可能性, 证实了简单的控制器可以实现自调频系统的同步. 在电路实验中, 压控振荡器用数字频率合成技术实现, 实验结果与理论分析一致. 关键词： 混沌 多稳态 峰值均值功率比 同步     

Modelling of chaotic systems based on modified weighted recurrent least squares support vector machines

Positive Lyapunov exponents cause the errors in modelling of the chaotic time series to grow exponentially. In this paper, we propose the modified version of the support vector machines (SVM) to deal with this problem. Based on recurrent least squares support vector machines (RLS-SVM), we introduce a weighted term to the cost function tocompensate the prediction errors resulting from the positive global Lyapunov exponents. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Santa Fe competition time series. The simulation results shows that the proposed method can capture the dynamics of the chaotic time series effectively.     

Analysis of Chaotic Dynamics by the Extended Entropic Chaos Degree

The Lyapunov exponent is the most-well-known measure for quantifying chaos in a dynamical system. However, its computation for any time series without information regarding a dynamical system is challenging because the Jacobian matrix of the map generating the dynamical system is required. The entropic chaos degree measures the chaos of a dynamical system as an information quantity in the framework of Information Dynamics and can be directly computed for any time series even if the dynamical system is unknown. A recent study introduced the extended entropic chaos degree, which attained the same value as the total sum of the Lyapunov exponents under typical chaotic conditions. Moreover, an improved calculation formula for the extended entropic chaos degree was recently proposed to obtain appropriate numerical computation results for multidimensional chaotic maps. This study shows that all Lyapunov exponents of a chaotic map can be estimated to calculate the extended entropic chaos degree and proposes a computational algorithm for the extended entropic chaos degree; furthermore, this computational algorithm was applied to one and two-dimensional chaotic maps. The results indicate that the extended entropic chaos degree may be a viable alternative to the Lyapunov exponent for both one and two-dimensional chaotic dynamics.     

Permutation entropy: a natural complexity measure for time series

We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.     

Cycles homoclinic to chaotic sets; robustness and resonance

For dynamical systems possessing invariant subspaces one can have a robust homoclinic cycle to a chaotic set. If such a cycle is stable, it manifests itself as long periods of quiescent chaotic behaviour interrupted by sudden transient 'bursts'. The time between the transients increases as the trajectory approaches the cycle. This behavior for a cycle connecting symmetrically related chaotic sets has been called 'cycling chaos' by Dellnitz et al. [IEEE Trans. Circ. Sys. I 42, 821-823 (1995)]. We characterise such cycles and their stability by means of normal Lyapunov exponents. We find persistence of states that are not Lyapunov stable but still attracting, and also states that are approximately periodic. For systems possessing a skew-product structure (such as naturally arises in chaotically forced systems) we show that the asymptotic stability and the attractivity of the cycle depends in a crucial way on what we call the footprint of the cycle. This is the spectrum of Lyapunov exponents of the chaotic invariant set in the expanding and contracting directions of the cycle. Numerical simulations and calculations for an example system of a homoclinic cycle parametrically forced by a Rossler attractor are presented; here we observe the creation of nearby chaotic attractors at resonance of transverse Lyapunov exponents. (c) 1997 American Institute of Physics.     

Characterization of chaos in a serrated plastic flow model

We report new results on a dynamical model of serrated yielding. These essentially pertain to the full spectrum of Lyapunov exponents of the non-linear (chaotic) model and fractal characterization of the associated strange attractor. The power spectrum of scalar time series extracted from the phase space trajectories decays exponentially with increase of frequency and the decay constant is found proportional to the Kolmogorov-Sinai entropy.     

Liu混沌系统的混沌分析及电路实验的研究

研究了一种新型混沌系统——Liu混沌系统的基本动力学行为以及电路实现的问题，给出了相图、庞卡莱映射、功率谱以及李雅普诺夫指数，基于李雅普诺夫指数谱和分叉图分析了系统参数对Liu混沌系统的影响.最后设计硬件电路证实了Liu混沌系统以及Liu混沌系统随系统参数变化时的各种状态的存在.给出数值仿真和电路实验的结果. 关键词： Liu混沌系统 分岔 电路实验     

How complex a dynamical network can be?

Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of a dynamical system. Not only they quantify how chaotic a dynamical system is, but since their sum is an upper bound for the rate of information production, they also provide a convenient way to quantify the complexity of a dynamical network. We conjecture based on numerical evidences that for a large class of dynamical networks composed by equal nodes, the sum of the positive Lyapunov exponents is bounded by the sum of all the positive Lyapunov exponents of both the synchronization manifold and its transversal directions, the last quantity being in principle easier to compute than the latter. As applications of our conjecture we: (i) show that a dynamical network composed of equal nodes and whose nodes are fully linearly connected produces more information than similar networks but whose nodes are connected with any other possible connecting topology; (ii) show how one can calculate upper bounds for the information production of realistic networks whose nodes have parameter mismatches, randomly chosen; (iii) discuss how to predict the behavior of a large dynamical network by knowing the information provided by a system composed of only two coupled nodes.     

The structure of a Lyapunov spectrum can be determined locally

One of the most important results of dynamical systems theory is the possibility to determine dynamical invariants by virtue of a long-term integration. In particular, this applies to the set of Lyapunov exponents of systems with chaotic solutions. However, we demonstrate that the structure of a Lyapunov spectrum, i.e., the signs of the (nonzero) exponents, is accessible already if the local flow is known within some small (in principle infinitesimal) time interval. We present various examples, including one in an embedding space, and discuss possible applications.     

Lagrangian chaos and the effect of drag on the enstrophy cascade in two-dimensional turbulence

We investigate the effect of drag force on the enstrophy cascade of two-dimensional Navier-Stokes turbulence. We find a power law decrease of the energy wave number (k) spectrum that is faster than the classical (no-drag) prediction of k(-3). It is shown that the enstrophy cascade with drag can be analyzed by making use of a previous theory for finite lifetime passive scalars advected by a Lagrangian chaotic fluid flow. Using this we relate the power law exponent of the energy wave number spectrum to the distribution of finite time Lyapunov exponents and the drag coefficient.     

采用振幅耦合方法研究多旋转中心混沌振子的相同步

为了找到具有多个旋转中心的混沌系统的相同步与其动力学拓朴变化之间的对应关系，采用线性振幅线性耦合方法，研究了Lorenz系统和Duffing系统的相同步，首先对Lorenz系统和Duffing系统分别进行极坐标变换，在线性振幅耦合基础上计算了两个系统的平均旋转数和Lyapunov指数，发现，随耦合强度的增大，系统相同步与系统的Lyapunov指数跃变存在一一对应的关系，这表明具有多个旋转中心的混沌系统的相同步与系统动力学拓朴变化也存在着对应关系. 关键词： Lyapunov指数 振幅耦合 相同步     

Low frequency oscillations in semi-insulating GaAs: a nonlinear analysis

We have observed low frequency current oscillations in a semi-insulating GaAs sample grown by low temperature molecular beam epitaxy. For this, an experimental setup proper to measure high impedance samples with small external noise was developed. Spontaneous oscillations in the current were observed for some bias conditions. Although measurements were carried out from room temperature down to liquid helium, the dynamical analysis was carried out around 200 K where the signal to noise ratio was fairly favorable. To increase the data quality we have also used a noise reduction algorithm suitably developed for nonlinear systems. We observed attractors having low embedding dimension, limit cycle bifurcations, and chaotic behavior characteristic of nonlinear dynamical processes in route to chaos. Attractor reconstruction, Poincare sections, Lyapunov exponents, and correlation dimension were also analyzed.     

Local Lyapunov exponents for spatiotemporal chaos

Local Lyapunov exponents are proposed for characterization of perturbations in distributed dynamical systems with chaotic behavior. Their relation to usual and velocity-dependent exponents is discussed. Local Lyapunov exponents are analytically calculated for coupled map lattices using random field approximation. Boundary Lyapunov exponents describing reflection of perturbations at boundaries are also introduced and calculated.     

Pseudo-periodic surrogate test to sample time series in stochastic softening Duffing oscillator

Identification of typical noise-contaminated sample response is a hard task in a nonlinear system under stochastic background since irregularity of the sample response may come from measure noise, dynamical noise, or nonlinear effect, etc., and conventional dynamical methods are generally not useful. Here, the pseudo-periodic surrogate algorithm by Small is employed to test the sample time series in the softening Duffing oscillator under the Gaussian white noise excitation. The correlation dimensions of the noisy periodic and the noise-induced chaotic time series of the system are compared with those of their corresponding surrogate data respectively, the leading Lyapunov exponents by Rosenstein's algorithm are also presented for comparison.     

Projecting low and extensive dimensional chaos from spatio-temporal dynamics

We review the spatio-temporal dynamical features of the Ananthakrishna model for the Portevin-Le Chatelier effect, a kind of plastic instability observed under constant strain rate deformation conditions. We then establish a qualitative correspondence between the spatio-temporal structures that evolve continuously in the instability domain and the nature of the irregularity of the scalar stress signal. Rest of the study is on quantifying the dynamical information contained in the stress signals about the spatio-temporal dynamics of the model. We show that at low applied strain rates, there is a one-to-one correspondence with the randomly nucleated isolated bursts of mobile dislocation density and the stress drops. We then show that the model equations are spatio-temporally chaotic by demonstrating the number of positive Lyapunov exponents and Lyapunov dimension scale with the system size at low and high strain rates. Using a modified algorithm for calculating correlation dimension density, we show that the stress-strain signals at low applied strain rates corresponding to spatially uncorrelated dislocation bands exhibit features of low dimensional chaos. This is made quantitative by demonstrating that the model equations can be approximately reduced to space independent model equations for the average dislocation densities, which is known to be low-dimensionally chaotic. However, the scaling regime for the correlation dimension shrinks with increasing applied strain rate due to increasing propensity for propagation of the dislocation bands. The stress signals in the partially propagating to fully propagating bands turn to have features of extensive chaos.