有限自动机产生的语言的度量熵

有限自动机理论在动力系统演化复杂性等方面有很大应用。本文讨论了有限自动机产生的语言的度量熵问题。给出了非常简洁的表达式。     

动力系统的语言复杂性

本文介绍以形式语言和自动机为主要工具来刻划动力系统的复杂性的研究情况。对于在动力系统中出现的形式语文指出了它们的一般特征，引进禁止字概念，指出了这类语言及禁止字集合之间的联系，综述了在区间单峰遇射方面已得到的结果。     

非线性时滞动力系统的研究进展

具有时滞的动力系统广泛存在于各工程领域．本文从动力学角度对时滞动力系统的研究进展作一综述,内容包括时滞动力系统的特点、研究方法、动力学热点问题的研究进展等．由于时滞动力系统的演化趋势不仅依赖于系统的当前状态,还依赖于系统过去某一时刻或若干时刻的状态,其运动方程要用泛国微分方程来描述,解空间是无穷维的．即使系统中的时滞非常小,在许多情况下也不能忽略不计．对于非线性时滞常微分方程,目前的研究思路基本上与常微分方程系统理论相平行．主要研究方法可分为时域法和频域法,前者包括Ｔａｙｌｏｒ级数法,中心流形法,Ｐｏｉｎｃａｒｅ映射法等,后者包括Ｎｙｑｕｉｓｔ法等．目前对这类系统的动力学研究主要集中在稳定性、Ｈｏｐｆ分岔、混沌等方面．研究表明：时滞动力系统具有非常丰富和复杂的动力学行为,如单变量的一维非线性时滞动力系统可发生混沌现象,与用常微分方程描述的系统有本质性差别．另一方面,人们可巧妙地利用时滞来控制动力系统的行为,如时滞反馈控制是控制混饨的主要方法之一．最后,本文展望了存在的一些问题以及近期值得关注的研究．     

复杂环境下无人机集群运动一致性的群体熵度量

针对无人机集群在多障碍物或空间受限等复杂环境中协同飞行时无法实现群体自主的协同演化及适应环境、态势的变化,导致整个群体运动产生振荡、失控等一致性差的问题,提出一种无人机集群运动一致性的群体熵度量理论。通过建立群体运动行为稳定程度的熵度量函数,作为个体之间协同运动的适应度值指标,实现复杂环境下无人机集群运动一致性的自适应调节。最后进行仿真实验,相比传统无人机集群算法,引入群体熵度量的无人机运动集聚收敛速度提高30%,复杂环境中集群运动的熵值波动范围下降21.8%,有效证明群体熵度量对于无人机集群运动一致性的提高。     

近岸非平整海底上耗散动力系统的广义波作用量守恒方程

为刻划近岸波-流-海底相互作用耗散动力系统的多种复杂作用机制，着眼于波浪对近岸大尺度变化环境流作用和考虑多变海底地形(可典型地刻划为由慢变水深和快变水深构成)的影响，由基于黏性流体Navie-Stokes方程的平均流方程，建立了近岸耗散动力系统的广义波作用量守恒方程，从中提出垂向速度波作用量和耗散波作用量这两种新概念，使得它们和经典的波作用量相互间达成了一种互补、协调而又主次分明的更为广泛的守恒形式．从而把波作用量这一经典概念从理想的平均流守恒系统引申到实际的平均流耗散系统(即广义守恒系统)中去，为解释沿岸过程和应用于近海、海岸工程提供了一个理论基础．     

关于非线性动力系统的数值追踪

一个依赖于参数的动力系统，当参数改变时，如何追踪它的行为，特别是如何得到它与初条件无关的稳定解集，是人们长期以来很感兴趣的问题，它不仅对离散动力系统有意义，对于连续动力系统，如结构、流体流动等也都可以通过离散化这类问题来处理，近廿年来，笔地得及其他作者从事这类问题的研究。并取得了一些初步结果。这里我们作一个简要的介绍。     

CML模型的时空混沌

提出了一类新的耦合映射格点（ＣＭＬ）模型．数值实验表明这类新的ＣＭＬ模型对于强弱耦合系统均能有效研究其时空复杂性，揭示了非线性、耗散、色散相互作用的非常丰富的时空Ｐａｔｅｒｎ行为     

热射流拟序结构中混沌现象的实验研究

就开放流体系统中的热射流拟序结构的混沌现象进行了实验研究．发现流场在绝对不稳定情况（S＜0.72）下,环涡模式控制了整个流场．此时相空间中对应的动力系统发生倍周期和Hopf分岔,说明动力系统可通过Feigenbaum和RTN途径进入混沌．相关维、相关熵和Lyapunov指数的计算表明：在一定Re数下,动力系统的时间渐近行为已呈混沌态,表现为奇异吸引子,相关维数D2约为3．80．     

大型结构动力响应的状态方程的Krylov精细时程积分法

提出了一种新的精细时程积分法来求解大型动力系统.结合Krylov子空间法、培德级数近似以及一般载荷的维数扩展法,进一步提高精细时程积分法的计算效率.利用维数扩展法避免计算微分方程特解,并可处理任意载荷.对于大型动力系统,通过Krylov子空间的降维分析将问题转化到一个子空间,计算效率得到极大提高.对于迭代次数N的选择作了详细讨论,进一步提高了计算效率.     

软件系统的复杂网络研究进展

互联网给软件带来了革命性的转变------软件网络化,这种趋势使软件作为全局性的资源,以网络为媒介向大众用户提供各种信息资源的应用服务.软件的计算模式、应用模式、产品形态以及盈利模式都会发生很大的变化,例如今后软件的应用方式就像打电话一样, 通过网络租用软件来实现.网络化软件正会成为联接各种网络资源、数据资源、计算资源的核心,成为数据和数据交换的基础. 同时, 网络化软件系统也将成为复杂系统,而复杂性也是软件开发困难、质量难以保证的关键.软件工程是将系统化、规范化、可度量的方法应用于软件的开发、运行和维护.复杂网络理论的最新研究成果,为复杂系统的软件工程提供了新的数学基础和方法. 分析了软件的复杂性,介绍了复杂网络与软件复杂性结合的研究工作,包括软件系统的拓扑特性、形成机理、演化规律以及软件复杂性度量和评估,对软件网络的研究现状进行了小结, 并列举了需要进一步研究的问题.提出软件网络观(软件在网络中生长、可以用网络来刻画软件)将有助于我们深入理解和认识软件的复杂性本质.      

Entropy measures for biological signal analyses

Entropies are among the most popular and promising complexity measures for biological signal analyses. Various types of entropy measures exist, including Shannon entropy, Kolmogorov entropy, approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and so on. A fundamental question is which entropy should be chosen for a specific biological application. To solve this issue, we focus on scaling laws of different entropy measures and introduce an ensemble forecasting framework to find the connections among them. One critical component of the ensemble forecasting framework is the scale-dependent Lyapunov exponent (SDLE), whose scaling behavior is found to be the richest among all the entropy measures. In fact, SDLE contains all the essential information of other entropy measures, and can act as a unifying multiscale complexity measure. Furthermore, SDLE has a unique scale separation property to aptly deal with nonstationarity and characterize high-dimensional and intermittent chaos. Therefore, SDLE can often be the first choice for exploratory studies in biology. The effectiveness of SDLE and the ensemble forecasting framework is illustrated by considering epileptic seizure detection from EEG.     

A novel construction of chaotic dynamics base gliders in diffusion rule B2/S7

The dynamical behaviors of gliders (mobile localizations) in diffusion rule B2/S7 are quantitatively analyzed from the theory of symbolic dynamics in two-dimensional symbolic sequence space. Their intrinsic complexity is demonstrated by exploiting the relationship between one-dimensional and two-dimensional subshifts. Based on this rigorous approach and technique, the underlying chaos of the extant gliders and their combinations is characterized in a subtle way. It is demonstrated that they can be identified to distinct subsystems with very rich and complicated dynamics; that is, diffusion rule is topologically mixing and possesses positive topological entropy on each subsystem. This analytical assertion provides the fact that diffusion rule is covered with complex subsystems “almost everywhere”. Finally, it is worth mentioning that the procedure proposed in this paper is also applicable to all other gliders arising from the two-dimensional cellular automata therein. It is an extended discovery in both cellular automata and chaos theory.     

Entropy of Dynamical Systems with Repetition Property

The repetition property of a dynamical system, a notion introduced in Boshernitzan and Damanik (Commun Math Phys 283:647–662, 2008), plays an importance role in analyzing spectral properties of ergodic Schrödinger operators. In this paper, entropy of dynamical systems with repetition property is investigated. It is shown that the topological entropy of dynamical systems with the global repetition property is zero. Minimal dynamical systems having both topological repetition property and positive topological entropy are constructed. This provides a class of ergodic Schrödinger operators with potentials generated by positive entropy minimal dynamical systems that, in contrast to common beliefs, admit no eigenvalues.     

The Complexity of Artificial Grammars

In experimental psychology, artificial grammars, generated by directed graphs, are used to test the ability of subjects to implicitly learn the structure of complex rules. We introduce the necessary notation and mathematics to view an artificial grammar as the sequence space of a dynamical system. The complexity of the artificial grammar is equated with the topological entropy of the dynamical system and is computed by finding the largest eigenvalue of an associated transition matrix. We develop the necessary mathematics and include relevant examples (one from the implicit learning literature) to show that topological entropy is easy to compute, well defined, and intuitive and, thereby, provides a quantitative measure of complexity that can be used to compare data across different implicit learning experiments.     

Rich dynamics caused by diffusion

It is an awfully difficult task to design an efficient numerical method for bifurcation diagrams, the graphs of Lyapunov exponents, or the topological entropy about discrete dynamical systems by linear/nonlinear diffusion with the Direchlet/Neumann- boundary conditions. Until now there are less works concerned with such a problem. In this paper, we propose a scheme about bifurcating analysis in a series of discrete-time dynamical systems with linear/nonlinear diffusion terms under the periodic boundary conditions. The complexity of dynamical behaviors caused by the diffusion term are to be determined. Bifurcation diagrams are shown by numerical simulation and chaotic behavior (chaotic Turing patterns) is demonstrated by computing the largest Lyapunov exponent. Our theoretical model can give an interesting case study about the phenomenon: the individuals exhibit a very simple dynamics but the groups with linear/nonlinear coupling can own a complex dynamics including fluctuation, periodicity and even chaotic behavior. We find that diffusion can trigger chaotic behavior in the present system and there exist multiple Turing patterns. It is interesting as regular or chaotic patterns can be reported in this study. Chaotic orbits emerge when exploring further in the diffusion coefficient space, and such a behavior is entirely absent in the corresponding continuous time-space system. The method proposed in the present paper is innovative and the conclusion is novel.

     

A Framework and Methodology for the Study of Nonlinear,Self-Organizing Family Dynamics

Family systems theories have emerged over the past 30 to 40 years primarily through clinical observations, resulting in diverse and internally inconsistent views of family structures, development, dynamics, and pathology; as well as a separation from more empirically based small group research. The 5-R's model is intended to unify the various family systems theories and render them more empirically testable using concepts and methodologies from non-linear dynamical systems theory. The conversation of one family was analyzed using orbital decomposition as a pilot test of the most basic assumptions of the 5-R's model. An optimal string length of three was found along with evidence of coherent complexity (chaos), with Lyapunov dimensionality equal to 1.7 and Shannon's entropy equal to 8.68. Results are discussed with respect to further empirical validation of the 5-R's model and clinical uses of the model and orbital decomposition methodology in conjoint therapy.     

Sample entropy of ECG time series of fearful flyers: preliminary results

Research within the framework of the nonlinear dynamical systems (NDS) in the field of anxiety disorders has shown that greater irregularity/complexity appears in the output from healthy systems. In this study we measured the Heart rate variability (HRV) and the sample sntropy (SampEn) of the ECG mV time series of fearful flyers (N = 15) and a matched control group (N = 15) when confronted with three combinations of feared stimuli (pictures, sounds, and pictures with sounds) as well as relaxing stimuli (pictures and sounds). Fearful flyers had lower SampEn than controls in all conditions, including baseline. Non-phobics showed significant entropy decreases from baseline in two out of three exposure conditions. No differences on HRV were found between groups, and HRV was not sensitive to condition changes. The main finding of the study is that the SampEn calculated on very short ECG mV recordings (10 to 60 seconds, easy to obtain in clinical settings) may be a useful diagnostic measure since it can distinguish fearful from non-fearful flyers.     

Fundamental aspects of curvature indices for characterizing dynamical systems

It is important to characterize the properties of dynamical systems by a quantity that signifies their structural changes, in particular those associated with occurrence of chaos or other transitional behaviors. There are some well-known indices, such as Lyapunov exponent, fractal dimension, and Kolmogorov entropy, while in this article we use a new quantifier, named the curvature index, to study the dynamical systems. The curvature index (proposed by Chen and Chang in Chaos 22(2):371–383 2012) is defined as the limit of the average curvature of a trajectory during evolution for a dynamical system, which lumps all the bending effects of the trajectory to a number, and estimates its average size (such as an attractor) in virtue of an inscribed space ball. One may define N-1 curvature indices for an N-dimensional dynamical system. Once the system undergoes a structural change, there are corresponding changes in the first and/or higher curvatures. The study is aimed to examine fundamental aspects of the curvature indices with further applications to some outstanding examples of dynamical systems in the literature, in parallel to the analysis by the Lyapunov exponents.     

Introduction to Nonlinear Dynamics of Electronic Systems: Tutorial

The study of chaos has generated enormous interest in exploring the complexity of the behavior in nature and in technology. Many of the important features of chaotic dynamical systems can be seen using experimental and computational methods in simple nonlinear mechanical systems or electronic circuits. Starting with the study of a chaotic nonlinear mechanical system (driven damped pendulum) or a nonlinear electronic system (circuit Chua) we introduce the reader into the concepts of chaos order in Sharkovsky's sense, and topological invariants (topological entropy and topological frequencies). The Kirchhoff's circuit laws are a pair of laws that deal with the conservation of charge and energy in electric circuits, and the algebraic theory of graphs characterizes these linear systems in terms of cycles and cocycles (or cuts). Here we discuss methods (topological semiconjugacy to piecewise linear maps and Markov graphs) to find a similar situation for the nonlinear dynamics, to understanding chaotic dynamics. Thus to chaotic dynamics we associate a Markov graph, where the dynamical and topological invariants will be seen as graph theoretical quantities.     

Synchronization control of oscillator networks using symbolic regression

Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far-reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly effective and interpretable control functions for such systems.