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.     

格子复杂性和符号序列的细粒化

提出一种新的有限长一维符号序列的复杂性度量——格子复杂性，建立在Lempel Ziv复杂性和一维迭代映射系统的符号动力学基础上.同时提出了符号序列的细粒化方法，可与格子复杂性以及Lempel Ziv复杂性结合.新度量在细粒化指数较小时与Lempel Ziv复杂性基本一致，在细粒化指数增大时显示出截然不同的特性.以Logistic映射为对象的计算实验表明，格子复杂性对混沌区的边缘最敏感.最后还讨论了上述复杂性度量的其他一些重要性质. 关键词： 混沌 复杂性度量 格子复杂性 细粒化     

Globally enumerating unstable periodic orbits for observed data using symbolic dynamics

The unstable periodic orbits of a chaotic system provide an important skeleton of the dynamics in a chaotic system, but they can be difficult to find from an observed time series. We present a global method for finding periodic orbits based on their symbolic dynamics, which is made possible by several recent methods to find good partitions for symbolic dynamics from observed time series. The symbolic dynamics are approximated by a Markov chain estimated from the sequence using information-theoretical concepts. The chain has a probabilistic graph representation, and the cycles of the graph may be exhaustively enumerated with a classical deterministic algorithm, providing a global, comprehensive list of symbolic names for its periodic orbits. Once the symbolic codes of the periodic orbits are found, the partition is used to localize the orbits back in the original state space. Using the periodic orbits found, we can estimate several quantities of the attractor such as the Lyapunov exponent and topological entropy.     

Symbolic Analysis of the Hénon Map at a = 1.4 and b = 0.3

The symbolic dyhamics of the Lozi map is applied to the Hgnon map at a = 1.4 and b = 0.3. The fundamental forbidden zone, inside which points correspond to forbidden symbolic sequences, is constructed from a numerically determined bartition line. All the allowed periodic orbits up to period 7 are determined, and chaotic orbits are derived.     

动力学变换法探测连续系统不稳定周期轨道

本文利用动力学变换方法和庞加莱截面方法对两种连续混沌动力学系统进行不稳定周期轨道探测研究, 并对Lorenz系统进行了替代数据法检验.结果表明:基于庞加莱截面的动力学变换改进算法 可有效探测连续混沌动力学系统中的不稳定周期轨道.     

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 Analysis of NMR-Laser Chaos

For the extended Bloch-type model of the NMR laser, a binary partition is determined directly from tangencies between forward and backward foliations of the Poincar6 map. Both forward and backward foliations are well ordered according to their symbolic sequences as those of the H6non map with a positive Jacobian. Based on the symbolic dynamics of the H6non map the admissibility condition for allowed sequences is derived and admissible periods are obtained, among which some were formally regarded as forbidden. The allowed periodic orbits are numerically verified. Allowed chaotic sequences are also constructed.     

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     

Symbolic Dynamics for the Sin Map

By investigating the sin map, the symbolic dynamics on the two-dimensional map was generalized. We constructed the infinite-word symbolic dynamics, and reduced it to the definiteword symbolic dynamics composed of a suitable number of the words. In the examples of the symbol analysis, all the permitted periodic objects up to period four were identified, and some chaotic trajectors were derived.     

一种确定混沌伪随机序列复杂度的模糊关系熵测度

将模糊关系的概念引入混沌伪随机序列复杂度的测度方法之中,提出了一种新的混沌伪随机序列复杂度测度方法——模糊关系熵(fuzzy relationship entropy,简记为F-REn)测度方法,并推导了F-REn的两个基本性质.仿真结果表明,该测度方法能够有效测度混沌伪随机序列的复杂度,与近似熵(ApEn)测度方法和符号熵测度方法相比,F-REn测度具有更加好的对序列符号空间的适用性、更加小的对测量维度的敏感性和更加强的对分辨率参数的鲁棒性. 关键词： 混沌伪随机序列 模糊理论 复杂度     

Unstable periodic orbits and noise in chaos computing

Different methods to utilize the rich library of patterns and behaviors of a chaotic system have been proposed for doing computation or communication. Since a chaotic system is intrinsically unstable and its nearby orbits diverge exponentially from each other, special attention needs to be paid to the robustness against noise of chaos-based approaches to computation. In this paper unstable periodic orbits, which form the skeleton of any chaotic system, are employed to build a model for the chaotic system to measure the sensitivity of each orbit to noise, and to select the orbits whose symbolic representations are relatively robust against the existence of noise. Furthermore, since unstable periodic orbits are extractable from time series, periodic orbit-based models can be extracted from time series too. Chaos computing can be and has been implemented on different platforms, including biological systems. In biology noise is always present; as a result having a clear model for the effects of noise on any given biological implementation has profound importance. Also, since in biology it is hard to obtain exact dynamical equations of the system under study, the time series techniques we introduce here are of critical importance.     

Symbolic dynamics of one-dimensional maps: Entropies,finite precision,and noise

In the study of nonlinear physical systems, one encounters apparently random or chaotic behavior, although the systems may be completely deterministic. Applying techniques from symbolic dynamics to maps of the interval, we compute two measures of chaotic behavior commonly employed in dynamical systems theory: the topological and metric entropies. For the quadratic logistic equation, we find that the metric entropy converges very slowly in comparison to maps which are strictly hyperbolic. The effects of finite precision arithmetric and external noise on chaotic behavior are characterized with the symbolic dynamics entropies. Finally, we discuss the relationship of these measures of chaos to algorithmic complexity, and use algorithmic information theory as a framework to discuss the construction of models for chaotic dynamics.     

基于符号分析的极大联合熵延迟时间求取方法

本文通过符号分析法求取联合熵的极大值点, 进而得到相空间重构的最佳延迟时间, 通过对几个典型的混沌系统进行数值仿真试验, 结果表明, 该方法简化了计算, 提高了效率, 能够准确快速地获得最佳延迟时间, 从而有效地重构原系统的相空间, 为混沌信号识别提供一种快速有效的途径. 关键词： 相空间重构 延迟时间 联合熵 符号分析     

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.     

基于在线最小二乘支持向量机回归的混沌时间序列预测

提出了一种基于在线最小二乘支持向量机（LS-SVM）回归的混沌时间序列的预测方法.与离线支持向量机相比，在线最小二乘支持向量机预测方法即使当混沌系统的参数随时间变化时仍然有效.以Chen's混沌系统、Rssler混沌系统、Hénon映射及脑电(EEG)信号四种混沌时 间序列为例评估本文提出的预测方法，结果验证了其混沌时间序列预测的有效性. 关键词： 混沌时间序列 预测 在线学习 支持向量机     

A universal algorithm to generate pseudo-random numbers based on uniform mapping as homeomorphism

A specific uniform map is constructed as a homeomorphism mapping chaotic time series into [0,1] to obtain sequences of standard uniform distribution. With the uniform map, a chaotic orbit and a sequence orbit obtained are topologically equivalent to each other so the map can preserve the most dynamic properties of chaotic systems such as permutation entropy. Based on the uniform map, a universal algorithm to generate pseudo random numbers is proposed and the pseudo random series is tested to follow the standard 0-1 random distribution both theoretically and experimentally. The algorithm is not complex, which does not impose high requirement on computer hard ware and thus computation speed is fast. The method not only extends the parameter spaces but also avoids the drawback of small function space caused by constraints on chaotic maps used to generate pseudo random numbers. The algorithm can be applied to any chaotic system and can produce pseudo random sequence of high quality, thus can be a good universal pseudo random number generator.     

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.     

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.     

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.