表面催化反应模型中关联噪声诱导非平衡相变

建立含有关联噪声的双分子-单分子(DM)表面催化反应延迟反馈模型,该模型能同时显示一级和二级非平衡动力学相变,即在一级和二级非平衡动力学相变之间的反应窗口展现.讨论双分子在DM延迟反馈模型中两种吸附机制,即局域和随机吸附模型.研究结果表明:1)外部噪声及两噪声关联性致使反应窗口的宽度收缩;2)内部噪声对非平衡动力学相变行为的影响依赖两噪声关联性,即当两噪声负关联,内部噪声致使反应窗口的宽度变宽;而当两噪声正关联时,内部噪声致使反应窗口的宽度收缩;3)关联噪声致使反应窗口变化对DM模型中一级和二级非平衡动力学相变研究具有重要的科学意义.     

N-dimensional dynamical systems exploiting instabilities in full

We report experimental and numerical results showing how certain N-dimensional dynamical systems are able to exhibit complex time evolutions based on the nonlinear combination of N-1 oscillation modes. The experiments have been done with a family of thermo-optical systems of effective dynamical dimension varying from 1 to 6. The corresponding mathematical model is an N-dimensional vector field based on a scalar-valued nonlinear function of a single variable that is a linear combination of all the dynamic variables. We show how the complex evolutions appear associated with the occurrence of successive Hopf bifurcations in a saddle-node pair of fixed points up to exhaust their instability capabilities in N dimensions. For this reason the observed phenomenon is denoted as the full instability behavior of the dynamical system. The process through which the attractor responsible for the observed time evolution is formed may be rather complex and difficult to characterize. Nevertheless, the well-organized structure of the time signals suggests some generic mechanism of nonlinear mode mixing that we associate with the cluster of invariant sets emerging from the pair of fixed points and with the influence of the neighboring saddle sets on the flow nearby the attractor. The generation of invariant tori is likely during the full instability development and the global process may be considered as a generalized Landau scenario for the emergence of irregular and complex behavior through the nonlinear superposition of oscillatory motions. (c) 2000 American Institute of Physics.     

Specific external forcing of spatiotemporal dynamics in reaction-diffusion systems

Self-organization behavior and in particular pattern forming spatiotemporal dynamics play an important role in far from equilibrium chemical and biochemical systems. Specific external forcing and control of self-organizing processes might be of great benefit in various applications ranging from technical systems to modern biomedical research. We demonstrate that in a cellular chemotaxis system modeled by one-dimensional reaction-diffusion equations particular forms of spatiotemporal dynamics can be induced and stabilized by controlling spatially distributed influx patterns of a chemical species as a function of time. In our model study we show that a propagating wave with certain shape and velocity and static symmetrical and asymmetrical patterns can be forced and manipulated by numerically computing open-loop optimal influx controls.     

Resynchronizing dynamical systems

Resynchronizing dynamical systems are important for certain chaotic signal masking methods. We demonstrate the dependence of the resynchronizing property of linear dynamical systems on choices of coordinate systems. Using these insights, we demonstrate how a nonlinear system not previously known to be synchronizable can be used for chaotic signal masking.     

Synchronization of spatiotemporal chemical chaos using random signals

We report the synchronization of two uncoupled spatially extended chemical systems by superimposing identical external random signals to both of them. In one spatial dimension, under appropriate parameter conditions the model systems exhibits a transition to turbulence via backfiring of pulses. Implementing the non-vanishing random signal control to the underlying partial differential equations, synchronization is achieved not only for identical systems, but also for systems operating under unequal parameter values exhibiting a different dynamical behavior (generalized synchronization). Finally, synchronization is also achieved under the influence of a random signal superimposed globally, thus making it relevant to experimental situations.     

Aging in subdiffusion generated by a deterministic dynamical system

We investigate aging behavior in a simple dynamical system: a nonlinear map which generates subdiffusion deterministically. Asymptotic behaviors of the diffusion process are described using aging continuous time random walks. We show how these processes are described by an aging diffusion equation which is of fractional order. Our work demonstrates that aging behavior can be found in deterministic low dimensional dynamical systems.     

Real-time nonlinear feedback control of pattern formation in (bio)chemical reaction-diffusion processes: a model study

Theoretical and experimental studies related to manipulation of pattern formation in self-organizing reaction-diffusion processes by appropriate control stimuli become increasingly important both in chemical engineering and cellular biochemistry. In a model study, we demonstrate here exemplarily the application of an efficient nonlinear model predictive control (NMPC) algorithm to real-time optimal feedback control of pattern formation in a bacterial chemotaxis system modeled by nonlinear partial differential equations. The corresponding drift-diffusion model type is representative for many (bio)chemical systems involving nonlinear reaction dynamics and nonlinear diffusion. We show how the computed optimal feedback control strategy exploits the system inherent physical property of wave propagation to achieve desired control aims. We discuss various applications of our approach to optimal control of spatiotemporal dynamics.     

Dynamics and kinematics of simple neural systems

The dynamics of simple neural systems is of interest to both biologists and physicists. One of the possible roles of such systems is the production of rhythmic patterns, and their alterations (modification of behavior, processing of sensory information, adaptation, control). In this paper, the neural systems are considered as a subject of modeling by the dynamical systems approach. In particular, we analyze how a stable, ordinary behavior of a small neural system can be described by simple finite automata models, and how more complicated dynamical systems modeling can be used. The approach is illustrated by biological and numerical examples: experiments with and numerical simulations of the stomatogastric central pattern generators network of the California spiny lobster. (c) 1996 American Institute of Physics.     

双稳随机动力系统信号调制噪声效应的数值分析

用数值方法研究了双稳随机动力系统的信号调制噪声效应.结果表明，正弦信号在系统输出中的效应仍为正弦信号，白噪声的效应则为维纳过程，通过选择合适的系统参数，可以减小系统输出中信号和噪声之间的耦合效应，系统可以大大抑制噪声，从而在双稳系统中可以产生信号调制噪声效应. 关键词： 双稳系统 信号调制噪声效应 随机共振     

Manipulation of self-aggregation patterns and waves in a reaction-diffusion system by optimal boundary control strategies

Reaction-diffusion systems are of considerable importance in many areas of physical sciences. For many reasons, an external manipulation of the dynamics of these processes is desirable. Here we show numerically how spatiotemporal behavior like pattern formation and wave propagation in a two component nonlinear reaction-diffusion model of bacterial chemotaxis can be externally controlled. We formulate the control goal as an objective functional and apply numerical optimization for the solution of the resulting control problem.     

Monitoring changes in time of chaotic nonlinear systems

We extend our method for classifying signals from chaotic nonlinear dynamical systems to the problem of monitoring chaotic nonlinear dynamical systems with the goal of detecting that the state of a system has changed. One potential application would be to systems where the changes are not easily detectable by spectral analysis or other linear techniques. The method is expected to be most useful in comparison to other techniques when there are other signals or noise present, some of which have a broad band frequency spectrum, and the signal of interest is associated with either a low dimensional dynamical system or a low dimensional chaotic attractor. The method is applied to data from a laboratory model of a fluidized bed reactor and to data from a gyroscope as well as to numerically generated signals from mathematical models. For the dynamical systems considered in the paper, the proposed method provides significantly better discrimination than spectral analysis. (c) 1995 American Institute of Physics.     

Coherence resonance and stochastic synchronization in a nonlinear circuit near a subcritical Hopf bifurcation

We analyze noise-induced phenomena in nonlinear dynamical systems near a subcritical Hopf bifurcation. We investigate qualitative changes of probability distributions (stochastic bifurcations), coherence resonance, and stochastic synchronization. These effects are studied in dynamical systems for which a subcritical Hopf bifurcation occurs. We perform analytical calculations, numerical simulations and experiments on an electronic circuit. For the generalized Van der Pol model we uncover the similarities between the behavior of a self-sustained oscillator characterized by a subcritical Hopf bifurcation and an excitable system. The analogy is manifested through coherence resonance and stochastic synchronization. In particular, we show both experimentally and numerically that stochastic oscillations that appear due to noise in a system with hard excitation, can be partially synchronized even outside the oscillatory regime of the deterministic system.     

Transverse effects in photorefractive two-wave mixing

In a biased photorefractive crystal, the process of two one-dimensional waves mixing, i.e., the dynamical evolution of both pump beam and signal beam, is traced by numerically solving the coupled-wave equation. Direct simulations show that the propagation and stability of the two beams are completely determined by the system parameters, such as the external bias field, the intensity and the beam waist of the pump beam. By adjusting these parameters, one can control the state of two Gaussian waves mixing. The numerical results are helpful for performing a two-wave mixing experiment.     

Birkhoff系统的离散最优控制及其在航天器交会对接中的应用

由于非线性,最优控制问题通常依赖于数值求解,即通过离散目标泛函和受控运动方程转化为一有限维的非线性最优化问题.最优控制问题中的受控运动方程在表示为受控Birkhoff方程的形式之后,可以利用受控Birkhoff方程的离散变分差分格式进行离散.与按照传统差分格式近似受控运动方程相比,此途径可以诱导更加真实可靠的非线性最优化问题,进而也会诱导更加精确有效的离散最优控制.应用于航天器交会对接问题,该种数值求解最优控制问题的方法在较大时间步长的情况下仍然求得了一个有效实现交会对接的离散最优控制.模拟结果验证了该方法的有效性.     

Control of dynamical instability in semiconductor quantum nanostructures diode lasers: Role of phase-amplitude coupling

We numerically investigate the complex nonlinear dynamics for two independently coupled laser systems consisting of (i) mutually delay-coupled edge emitting diode lasers and (ii) injection-locked quantum nanostructures lasers. A comparative study in dependence on the dynamical role of α parameter, which determine the phase-amplitude coupling of the optical field, in both the cases is probed. The variation of α lead to conspicuous changes in the dynamics of both the systems, which are characterized and investigated as a function of optical injection strength η for the fixed coupled-cavity delay time τ. Our analysis is based on the observation that the cross-correlation and bifurcation measures unveil the signature of enhancement of amplitude-death islands in which the coupled lasers mutually stay in stable phase-locked states. In addition, we provide a qualitative understanding of the physical mechanisms underlying the observed dynamical behavior and its dependence on α. The amplitude death and the existence of multiple amplitude death islands could be implemented for applications including diode lasers stabilization.     

一种改进的高性能Lorenz系统构造及其应用

Lorenz系统是一种最具有代表性、典型性的混沌模型之一, 一直被众多学者深入研究和广泛应用.为了获取结构和动力学行为更为复杂的混沌吸引子, 不断改善Lorenz系统已成为混沌动力系统研究中的重要课题之一. 为此, 本文提出了一个具有复杂系统动力学行为的改进的Lorenz系统, 并将其用于图像信息安全保护. 在现有各种改进的Lorenz系统的基础上, 首先通过增加Lorenz系统的控制参数和改变非线性项相结合的方法构造出一种新的Lorenz 混沌系统; 其次采用微分动力系统方法深入研究该系统并获得与Lorenz系统、Bao系统、Tee系统和Y系统等具有相似的耗散性、对称性、稳定性, 以及更加复杂的混沌特性和动力学行为, 同时分析该系统所产生随机序列具有良好的相关性和复杂性; 最后将其所产生的离散伪随机序列用于图像置乱和扩散加密, 通过对图像加密结果的相邻像素相关性分析、灰度空间相关特性不确定性分析、抗差分攻击以及密钥敏感性测试, 表明本文所构造的改进的Lorenz系统应用于图像加密能获得相对较高的安全性.     

Classical and fractal analysis of vehicle demand on the ferry-boat system

Transportation problems are important complex systems because of the increased number of vehicles in cities. In this paper, we study time series of vehicle demand by using the ferry-boat system between Salvador city and Itaparica island, in Bahia, Brazil. We compare the traditional demand analysis (ARIMA method) with the self-affine ones (the scaling exponent α and the density of crossing points ρ). In addition, taking into account the inherent self-affine behavior we study the stationary states of this dynamic process by using a nonlinear Fokker-Planck equation. The present findings indicate that the scaling exponent α describes some properties of flux of vehicles using the ferry-boat system. The behavior of α gives an alternative explanation about demand analysis, and the nonlinear Fokker-Planck equation presents a solution close to the stationary behavior of this complex dynamical analysis.     

Microchameleons: nonlinear chemical microsystems for amplification and sensing

In biological systems, the coupling of nonlinear biochemical kinetics and molecular transport enables functional sensing and "signal" amplification across many length scales. Drawing on biological inspiration, we describe how artificial reaction-diffusion (RD) microsystems can provide a basis for sensing applications, capable of amplifying micro- and nanoscopic events into macroscopic visual readouts. The RD applications reviewed here are based on a novel experimental technique, WETS for Wet Stamping, which offers unprecedented control over RD processes in microscopic and complex geometries. It is discussed how RD can be used to sense subtle differences in the thickness and/or absorptivity of thin absorptive films, amplify macromolecular phase transitions, detect the presence and quality of self-assembled monolayers, and provide dynamic spatiotemporal readouts of chemical "metabolites."     

Synchronization from disordered driving forces in arrays of coupled oscillators

The effects of disorder in external forces on the dynamical behavior of coupled nonlinear oscillator networks are studied. When driven synchronously, i.e., all driving forces have the same phase, the networks display chaotic dynamics. We show that random phases in the driving forces result in regular, periodic network behavior. Intermediate phase disorder can produce network synchrony. Specifically, there is an optimal amount of phase disorder, which can induce the highest level of synchrony. These results demonstrate that the spatiotemporal structure of external influences can control chaos and lead to synchronization in nonlinear systems.     

Some Problems of Information Neurodynamics

The goal of neural science is to understand the brain, how we perceive, move, think, and remember. All of these things are dynamical processes which are taking place in a complex, non-stationary and noisy environment. This means that these dynamical processes at all levels from small neural networks to behavior should be stable against perturbations but flexible and adaptive. The goal of neurodynamics is to formulate the main dynamical principles which can be a basis of such behavior and to predict the possible activities of neurons and neural ensembles using the tools of nonlinear dynamics. In this paper we discuss our last results related to the mostly challenging part of neurodynamics: information processing by dynamical neural ensembles.