基于符号向量动力学的耦合映像格子初始向量估计

在符号动力学的基础上,探讨了基于符号向量序列的局部耦合映像格子求逆问题,证明了相空间I^N上任意取值通过基于符号向量序列的逆迭代过程必然收敛至初始向量,提出了基于符号向量动力学的初始向量估计算法,从而建立了耦合映像格子符号序列和实际动力系统相空间的对应关系.实验结果表明,根据该算法可以有效建立符号向量序列和耦合映像格子相空间之间的对应关系,更好地刻画了实际模型的物理过程. 关键词： 耦合映像格子 符号动力学 初始向量估计     

基于符号动力学的耦合映像格子系统的初值估计

在符号动力学的基础上,深入探讨了基于动力学符号序列的局部耦合映像格子系统求逆问题.在理论上系统地分析耦合映像系统初值估计的性能与耦合系数及映射函数之间的数学关系,证明相空间I^M上的任意取值通过基于符号向量序列的逆迭代过程并不一定收敛至初值,其敛散性与耦合强度和映射函数的选择有直接关系.同时证明了混沌或其拓扑共轭的逆不一定为压缩映射,其总体的敛散性与整个逆迭代过程中的收敛与发散的强度对比有关.理论分析与数值实验结果完全一致,说明本文提出的耦合映像格子系统初值估计问题的分析 关键词： 耦合映像格子 符号动力学 初值估计     

基于时变耦合映像格子模型的信号初值估计

从耦合映像格子中,恢复系统初始条件是耦合系统求逆问题,也是信号处理研究中的一个关键性问题.本文在符号动力学方法的基础上,对映像系数进行修正,针对耦合单峰Logistic映射,提出一种基于时变映像系数恢复信号初值的新方法.在映像过程无噪或受到高斯白噪声污染时,本文方法都能够较好地恢复信号初值的统计特性,而且具有较小的偏差和均方误差,并与原信号之间具有较强的相关性,从而能够更好和更加合理地刻画实际信号的物理过程,对系统初值做出更优的估计. 关键词： 耦合映像格子 恢复初值的统计特性 时变映像系数     

Lorenz系统驱动声光双稳时空混沌系统并行同步的研究

研究了一个时间混沌系统驱动多个时空混沌系统的并行同步问题.以单模激光Lorenz系统和一维耦合映像格子为例,在单模激光Lorenz系统中提取一个混沌序列,通过与一维耦合映像格子中的状态变量耦合使单模激光Lorenz系统和多个同结构一维耦合映像格子同时达到广义同步,并且多个一维耦合映像格子之间实现完全并行同步.通过计算条件Lyapunov指数,可以得到并行同步所需反馈系数的取值范围.数值模拟证明了此方法的可行性和有效性.     

一个全局耦合不连续映像格子中的冻结化随机图案模式

本文研究了一类既不连续又不可逆分段线性映像构成的全局耦合映像格子系统中的一类典型集体动力学行为, 即冻结化随机图案模式. 计算了平均同步序参量和最大李雅普诺夫指数随耦合强度的变化. 结果显示, 当耦合强度超过某个阈值后, 在给定动力学变量的初始下, 系统几乎都能达到完全或部分同步状态, 出现冻结化随机图案. 这些现象表明, 耦合映像格子系统中存在着多个共存的吸引子. 因此, 其冻结化图案的结构和分布敏感地依赖于格点动力学变量初始值的选取. 感兴趣地是, 即使当单映像处于混沌状态时, 格点间的耦合仍能将系统调制到规则的运动状态, 这种特征对于混沌控制具有重要的利用价值. 上述丰富动力学行为的出现是由于单映像中不连续性和不可逆性相互作用的结果.     

混沌伪随机序列复杂度分析的符号动力学方法

通过将混沌伪随机序列看成一个符号序列，提出了用符号动力学的 方法来分析混沌伪随机序列的复杂度.以Logistic映射和耦合映射格子系统产生的混沌伪随 机序列为例，说明了该方法的应用，并将计算结果与近似熵ApEn法的计算结果作了比较.结 果表明，该方法可以有效地判断出不同的混沌伪随机序列的复杂程度，而且比近似熵法更为 优越. 关键词： 混沌 伪随机序列 符号动力学 熵     

一个全局耦合不连续映像格子中的奇异态

文章研究了一类由既不可逆又不连续映像构成的全局耦合映像格子系统中的奇异态行为,计算了系统的同步序参量和空间振幅变化图.结果表明,在某些特定的参数区间内,耦合映像格子系统会出现奇异态或团簇态,并且敏感地依赖于耦合强度的选择.上述丰富的动力学现象是由于单映像中不连续、不可逆性以及空间耦合相互作用的结果.通过数值模拟找到了奇异态或团簇态出现的特定参数区域.     

基于多重耦合映像格子的信号初值恢复研究

利用耦合映像格子恢复信号初值是信号处理研究中一个重要的问题.耦合映像格子具有混沌系统的初值敏感性，当初值受到噪声污染时将会影响到系统对其的恢复.提出了一种由多个一维耦合映像格子系统并列耦合而成的多重耦合映像格子系统，通过将多个一维系统耦合，使因受到噪声干扰而趋向于指数分离的混沌轨道相互靠近，以达到抑制噪声的目的.数值仿真表明，该系统具有较强的抗噪声能力和较高的鲁棒性.在耦合系数选取适当的情况下，即使初始信号受到噪声干扰，该多重耦合系统仍然能够很好地恢复信号初值的统计特性，且对单个初值的恢复情况及与初始信号 关键词： 耦合映像格子 恢复信号的统计特性 多重耦合     

从耦合映象格子中粗略恢复初值的统计特性

耦合映象格子用于信号处理研究时，从中恢复出初始条件是一个非常重要的问题.提出一种粗略恢复格点初值的方法，数值实验表明，动力学函数使用Logistic映射时，在映象过程不存在噪声的情况下，恢复的整个格子初始信号平均值等于给定信号分布的真实平均值，而恢复信号的方差小于给定信号的真实方差.将耦合看作是对独立映射的一种变换，对此作了初步解释，同时发现Logistic映射不同参数下的符号序列排序存在一些有趣的规律.对耦合格子映射研究、非线性耦合则量等是非常有启发意义的. 关键词： 耦合映射格子 信号恢复的统计特性     

基于可变参数双向耦合映像系统的时空混沌Hash函数设计

在分析单向与双向耦合映像格子系统的初值与参数敏感性的基础上，提出了一种基于可变参数双向耦合映像系统的时空混沌单向Hash函数构造方案.该方案以耦合映像系统的部分初态作为密钥，在迭代过程中, 通过上一次的迭代值和线性变换后的不同位置的明文消息比特动态确定双向耦合映像系统模型参数，将明文消息多格点并行注入时空混沌轨迹中；取迭代序列中最后一轮迭代结果的适当空间项，线性映射为Hash值要求的128 bit值.由于耦合映像系统的双向扩散机理与混乱作用，迭代过程具有极强的不可逆性及初值与参数敏感性，Hash结果的每位都与明文及密钥有着敏感、复杂的非线性强耦合关系.仿真实验与分析结果表明，该算法达到了Hash函数的各项性能要求，安全性好，执行效率高. 关键词： Hash函数 时空混沌 耦合映像格子     

Estimation of chaotic coupled map lattices using symbolic vector dynamics

In [K. Wang, W.J. Pei, Z.Y. He, Y.M. Cheung, Phys. Lett. A 367 (2007) 316], an original symbolic vector dynamics based method has been proposed for initial condition estimation in additive white Gaussian noisy environment. The estimation precision of this estimation method is determined by symbolic errors of the symbolic vector sequence gotten by symbolizing the received signal. This Letter further develops the symbolic vector dynamical estimation method. We correct symbolic errors with backward vector and the estimated values by using different symbols, and thus the estimation precision can be improved. Both theoretical and experimental results show that this algorithm enables us to recover initial condition of coupled map lattice exactly in both noisy and noise free cases. Therefore, we provide novel analytical techniques for understanding turbulences in coupled map lattice.     

A method of recovering the initial vectors of globally coupled map lattices based on symbolic dynamics

Based on symbolic dynamics, a novel computationally efficient algorithm is proposed to estimate the unknown initial vectors of globally coupled map lattices (CMLs). It is proved that not all inverse chaotic mapping functions are satisfied for contraction mapping. It is found that the values in phase space do not always converge on their initial values with respect to sufficient backward iteration of the symbolic vectors in terms of global convergence or divergence (CD). Both CD property and the coupling strength are directly related to the mapping function of the existing CML. Furthermore, the CD properties of Logistic, Bernoulli, and Tent chaotic mapping functions are investigated and compared. Various simulation results and the performances of the initial vector estimation with different signal-to-noise ratios (SNRs) are also provided to confirm the proposed algorithm. Finally, based on the spatiotemporal chaotic characteristics of the CML, the conditions of estimating the initial vectors using symbolic dynamics are discussed. The presented method provides both theoretical and experimental results for better understanding and characterizing the behaviours of spatiotemporal chaotic systems.     

Studies of phase return map and symbolic dynamics in a periodically driven Hodgkin–Huxley neuron

How neuronal spike trains encode external information is a hot topic in neurodynamics studies.In this paper,we investigate the dynamical states of the Hodgkin–Huxley neuron under periodic forcing.Depending on the parameters of the stimulus,the neuron exhibits periodic,quasiperiodic and chaotic spike trains.In order to analyze these spike trains quantitatively,we use the phase return map to describe the dynamical behavior on a one-dimensional(1D)map.According to the monotonicity or discontinuous point of the 1D map,the spike trains are transformed into symbolic sequences by implementing a coarse-grained algorithm—symbolic dynamics.Based on the ordering rules of symbolic dynamics,the parameters of the external stimulus can be measured in high resolution with finite length symbolic sequences.A reasonable explanation for why the nervous system can discriminate or cognize the small change of the external signals in a short time is also presented.     

Symbolic synchronization and the detection of global properties of coupled dynamics from local information

We study coupled dynamics on networks using symbolic dynamics. The symbolic dynamics is defined by dividing the state space into a small number of regions (typically 2), and considering the relative frequencies of the transitions between those regions. It turns out that the global qualitative properties of the coupled dynamics can be classified into three different phases based on the synchronization of the variables and the homogeneity of the symbolic dynamics. Of particular interest is the homogeneous unsynchronized phase, where the coupled dynamics is in a chaotic unsynchronized state, but exhibits qualitative similar symbolic dynamics at all the nodes in the network. We refer to this dynamical behavior as symbolic synchronization. In this phase, the local symbolic dynamics of any arbitrarily selected node reflects global properties of the coupled dynamics, such as qualitative behavior of the largest Lyapunov exponent and phase synchronization. This phase depends mainly on the network architecture, and only to a smaller extent on the local chaotic dynamical function. We present results for two model dynamics, iterations of the one-dimensional logistic map and the two-dimensional Henon map, as local dynamical function.     

Estimating good discrete partitions from observed data: symbolic false nearest neighbors

A symbolic analysis of observed time series requires a discrete partition of a continuous state space containing the dynamics. A particular kind of partition, called "generating," preserves all deterministic dynamical information in the symbolic representation, but such partitions are not obvious beyond one dimension. Existing methods to find them require significant knowledge of the dynamical evolution operator. We introduce a statistic and algorithm to refine empirical partitions for symbolic state reconstruction. This method optimizes an essential property of a generating partition, avoiding topological degeneracies, by minimizing the number of "symbolic false nearest neighbors." It requires only the observed time series and is sensible even in the presence of noise when no truly generating partition is possible.     

Editorial

Symbolic dynamics is a powerful tool in the study of dynamical systems. The purpose of symbolic dynamics is to provide a simplified picture of complicated dynamics, that gives some insight into its complexity. To this end, the state space of the system is partitioned in a finite number of pieces, and the exact trajectories of individual points are traded off by the trajectory relative to that partition. These so-called coarse-grained trajectories turn out to be realisations of a stationary random process with a finite alphabet. In particular, the entropy of a dynamical system can be approximated by the Shannon entropy of any of its symbolic dynamics (the finer the partition, the better the approximation). Today, symbolic dynamics is an independent field of theoretical physics and applied mathematics with applications to such important disciplines as cryptology, time series analysis, and data-compression.