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     

气液两相流电导波动信号复杂性测度分析及其流型表征

为了考察从时间序列提取的复杂性测度与气液两相流流型变化之间的关系，本文首先讨论了三种复杂性测度(Lempel-Ziv复杂性、功率谱熵和近似熵)对周期信号、随机信号、混合随机信号和混沌信号的识别能力，然后分析了时间序列长度对复杂性计算的影响.在此基础上，从实际测量的80种垂直上升管中气液两相流电导波动信号中提取了这三种复杂性测度，结果表明：三种复杂度对两相流流型变化是敏感的，通过对三种复杂度随两相流流动参数变化规律分析，可以得到气液两相流动力学结构反演特征，为揭示气液两相流流型转化机理提供了一种有效的辅助诊断工具. 关键词： 气液两相流 Lempel和Ziv复杂性 功率谱熵 近似熵     

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.     

基于时空混沌系统的单向Hash函数构造

提出了一种基于时空混沌系统的Hash函数构造方法.以线性变换后的消息数作为一组初值来驱动单向耦合映像格子的时空混沌系统，产生时空混沌序列，取其空间最后一组混沌序列的适当项，线性映射为Hash值要求的128bit值.研究结果表明，这种基于时空混沌系统的Hash函数具有很好的单向性、弱碰撞性、初值敏感性，较基于低维混沌映射的Hash函数具有更强的保密性能，且实现简单. 关键词： 时空混沌 Hash函数 单向耦合映像格子     

级联混沌及其动力学特性研究

初值敏感性是混沌的本质,混沌的随机性来源于其对初始条件的高度敏感性,而Lyapunov指数又是这种初值敏感性的一种度量.本文的研究发现,混沌系统的级联可明显提高级联混沌的Lyapunov指数,改善其动力学特性.因此,本文研究了混沌系统的级联和级联混沌对动力学特性的影响,提出了混沌系统级联的定义及条件,从理论上证明了级联混沌的Lyapunov指数为各个级联子系统Lyapunov指数之和;适当的级联可增加系统参数、扩展混沌映射和满映射的参数区间,由此可提高混沌映射的初值敏感性和混沌伪随机序列的安全性.以Logistic映射、Cubic映射和Tent映射为例,研究了Logistic-Logistic级联、Logistic-Cubic级联和Logistic-Tent级联的动力学特性,验证了级联混沌动力学性能的改善.级联混沌可作为伪随机数发生器的随机信号源,用以产生初值敏感性更高、安全性更好的伪随机序列.     

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     

用符号分析法显示时空混沌信号之间的相互关系

基于耦合映射格子模型, 用符号分析法研究了时空混沌信号的相互关系. 数值计算结果表明, 两个直接耦合的格子之间或两个未直接耦合但邻近的格子之间的条件熵存在一个尖锐的极小值, 而两个距离比较远的格子之间的条件熵则没有尖锐的极小值. 因此, 耦合映射格子系统产生的时空混沌信号之间的相互关系可以通过符号分析的方法显示出来.     

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     

基于符号向量动力学的耦合映像格子参数估计

本文,将符号动力学推广到耦合映像格子中,以Logistic映射下耦合映像格子为研究对象,研究控制参数对符号向量序列动力学特性的影响.通过研究耦合映像格子逆函数,给出耦合映像格子的遍历条件.进一步,将给出系统初始向量,禁止字以及控制参数的符号向量序列描述方法,并最终给出基于符号向量动力学的耦合映像格子控制参数估计方法.实验结果表明,根据本文算法可以有效建立符号序列和耦合映像格子控制参数之间的对应关系,能够更好地刻画了实际模型的物理过程. 关键词： 符号向量动力学 耦合映像格子 参数估计 遍历性     

TD-ERCS离散混沌伪随机序列的复杂性分析

采用相空间直接观察法和行为复杂性算法,系统地分析了新型TD-ERCS离散混沌系统产生的伪随机序列的复杂性,得出了其复杂性变化规律.在Kolmogorov复杂性基础上,应用经典的Limpel-Ziv算法,ApEn算法和PE算法,从一维时间序列到多维相空间重构两方面计算了TD-ERCS离散混沌伪随机序列的复杂度大小.计算结果表明,TD-ERCS系统的行为复杂性高,而且该系统的复杂性大小随系统参数改变的变化范围小,是一个复杂性非常稳定的全域性离散混沌系统,其产生的混沌伪随机序列适合于信息加密或扩频通信. 关键词： 混沌 混沌伪随机序列 TD-ERCS系统 复杂度     

A new complexity measure for time series analysis and classification

Complexity measures are used in a number of applications including extraction of information from data such as ecological time series, detection of non-random structure in biomedical signals, testing of random number generators, language recognition and authorship attribution etc. Different complexity measures proposed in the literature like Shannon entropy, Relative entropy, Lempel-Ziv, Kolmogrov and Algorithmic complexity are mostly ineffective in analyzing short sequences that are further corrupted with noise. To address this problem, we propose a new complexity measure ETC and define it as the “Effort To Compress” the input sequence by a lossless compression algorithm. Here, we employ the lossless compression algorithm known as Non-Sequential Recursive Pair Substitution (NSRPS) and define ETC as the number of iterations needed for NSRPS to transform the input sequence to a constant sequence. We demonstrate the utility of ETC in two applications. ETC is shown to have better correlation with Lyapunov exponent than Shannon entropy even with relatively short and noisy time series. The measure also has a greater rate of success in automatic identification and classification of short noisy sequences, compared to entropy and a popular measure based on Lempel-Ziv compression (implemented by Gzip).     

Interpreting non-random signatures in biomedical signals with Lempel-Ziv complexity

Lempel-Ziv complexity (LZ) [J. Ziv, A. Lempel, On the complexity of finite sequences, IEEE Trans. Inform. Theory 22 (1976) 75-81] and its variants have been used widely to identify non-random patterns in biomedical signals obtained across distinct physiological states. Non-random signatures of the complexity measure can occur under nonlinear deterministic as well as non-deterministic settings. Surrogate data testing have also been encouraged in the past in conjunction with complexity estimates to make a finer distinction between various classes of processes. In this brief letter, we make two important observations (1) Non-Gaussian noise at the dynamical level can elude existing surrogate algorithms namely: Phase-randomized surrogates (FT) amplitude-adjusted Fourier transform (AAFT) and iterated amplitude-adjusted Fourier transform (IAAFT). Thus any inference nonlinear determinism as an explanation for the non-randomness is incomplete (2) Decrease in complexity can be observed even across two linear processes with identical auto-correlation functions. The results are illustrated with a second-order auto-regressive process with Gaussian and non-Gaussian innovations. AR(2) processes have been used widely to model several physiological phenomena, hence their choice. The results presented encourage cautious interpretation of non-random signatures in experimental signals using complexity measures.     

Mixing Bandt-Pompe and Lempel-Ziv approaches: another way to analyze the complexity of continuous-state sequences

In this paper, we propose to mix the approach underlying Bandt-Pompe permutation entropy with Lempel-Ziv complexity, to design what we call Lempel-Ziv permutation complexity. The principle consists of two steps: (i) transformation of a continuous-state series that is intrinsically multivariate or arises from embedding into a sequence of permutation vectors, where the components are the positions of the components of the initial vector when re-arranged; (ii) performing the Lempel-Ziv complexity for this series of ‘symbols’, as part of a discrete finite-size alphabet. On the one hand, the permutation entropy of Bandt-Pompe aims at the study of the entropy of such a sequence; i.e., the entropy of patterns in a sequence (e.g., local increases or decreases). On the other hand, the Lempel-Ziv complexity of a discrete-state sequence aims at the study of the temporal organization of the symbols (i.e., the rate of compressibility of the sequence). Thus, the Lempel-Ziv permutation complexity aims to take advantage of both of these methods. The potential from such a combined approach – of a permutation procedure and a complexity analysis – is evaluated through the illustration of some simulated data and some real data. In both cases, we compare the individual approaches and the combined approach.     

Discrete Capability of the Lempel-Ziv Complexity Algorithm on a Vibration Sequence

The Lempel Ziv complexity （LZC） method is used to analyze the acceleration response of the T-shaped plate. The response is converted into symbolic sequences with the multi-segmented coarse-grained method. The LZC of the response of 240 points located in different areas near the center is calculated. The results show that LZC arithmetic applied to elastomer vibration can satisfy the discreteness condition.     

Lempel-Ziv复杂度算法中粗粒化方法分析及改进

为了提高Lempel-Ziv复杂度(LZC)的抗干扰能力和稳定性,提出用等概率粗粒化方法计算LZC的思想,介绍其具体算法,分析二值粗粒化阈值与LZC的关系.用Logistic映射生成87个序列进行抗干扰试验,计算这些序列加噪前后所得LZC序列的相关系数和相对变异系数,作为LZC指标抗干扰能力的测度,用10个脑电图进行LZC稳定性测试.结果表明,用等概率粗粒化方法时的相关系数都大于0.998,相对变异系数较小,脑电的LZC稳定性好.该方法可明显提高LZC的抗干扰能力和稳定性.     

非线性时间序列互信息与Lempel-Ziv复杂度的相关性研究

为探究非线性动力学系统的互信息和复杂度的相关性，用Logistic映射、Lorenz模型和心电RR间期的非线性时间序列作为实验数据，计算多分段延时互信息和多分段Lempel-Ziv复杂度以及它们之间的相关系数.结果表明这些序列的互信息和复杂度呈强负相关，对Logistic方程生成的201个序列的不同段互信息和不同段复杂度之间的相关系数绝对值都大于0.9162，最大达0.9923；对94个心电RR间期序列都大于0.8555，最大达0.9860.研究还发现互信息比复杂度能更敏感地表现出非线性动力系统的特征. 关键词： 相关系数 互信息 Lempel-Ziv 复杂度 心电RR间期     

A complexity measure approach based on forbidden patterns and correlation degree

Based on forbidden patterns in symbolic dynamics, symbolic subsequences are classified and relations between forbidden patterns, correlation dimensions and complexity measures are studied. A complexity measure approach is proposed in order to separate deterministic (usually chaotic) series from random ones and measure the complexities of different dynamic systems. The complexity is related to the correlation dimensions, and the algorithm is simple and suitable for time series with noise. In the paper, the complexity measure method is used to study dynamic systems of the Logistic map and the H\'enon map with multi-parameters.     

The Impact of the COVID-19 Pandemic on the Unpredictable Dynamics of the Cryptocurrency Market

The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.