Moment Lyapunov exponents of a two-dimensional system under both harmonic and white noise parametric excitations

The moment Lyapunov exponents and the Lyapunov exponents of a 2D system under both harmonic and white noise excitations are studied. The moment Lyapunov exponents and the Lyapunov exponents are important characteristics determining the moment and almost-sure stability of a stochastic dynamical system. The eigenvalue problem governing the moment Lyapunov exponent is established. A singular perturbation method is applied to solve the eigenvalue problem to obtain second-order, weak noise expansions of the moment Lyapunov exponents. The influence of the white noise excitation on the parametric resonance due to the harmonic excitation is investigated.     

Flow-induced instability under bounded noise excitation in cross-flow

Flow-induced vibration of a single cylinder in a cross-flow is mainly due to vortex shedding, which is usually considered as a forced vibration problem. It is shown that flow-induced vibration of a cylinder in the lock-in region is a combination of forced resonant vibration and fluid-damping-induced instability, which leads to time-dependent-fluid-damping-induced parametric resonance and constant-negative-damping-induced instability. The time-dependent fluid damping can be modeled as a bounded noise. The dynamic stability of a two-dimensional system under bounded noise excitation with a narrow-band characteristic is studied through the determination of the moment Lyapunov exponent and the Lyapunov exponent. The case when the system is in primary parametric resonance in the absence of noise is considered and the effect of noise on the parametric resonance is investigated. For small amplitudes of the bounded noise, analytical expansions of the moment Lyapunov exponents and Lyapunov exponents are obtained, which are shown to be in excellent agreement with those obtained using Monte Carlo simulation. The theory of stochastic stability is applied to explore the stability of a cylinder in a cross-flow. The analytical and numerical results show that the time-dependent-fluid-damping-induced parametric resonance could occur, which suggests that parametric resonance also contributes to the vibration of the cylinder in the lock-in range.     

Higher-order stochastic averaging to study stability of a fractional viscoelastic column

Stochastic stability of a fractional viscoelastic column axially loaded by a wideband random force is investigated by using the method of higher-order stochastic averaging. By modelling the wideband random excitation as Gaussian white noise and real noise and assuming the viscoelastic material to follow the fractional Kelvin–Voigt constitutive relation, the motion of the column is governed by a fractional stochastic differential equation, which is justifiably and uniformly approximated by an averaged system of Itô stochastic differential equations. Analytical expressions are obtained for the moment Lyapunov exponent and the Lyapunov exponent of the fractional system with small damping and weak random fluctuation. The effects of various parameters on the stochastic stability of the system are discussed.     

Noise induced destruction of zero Lyapunov exponent in coupled chaotic systems

Recently, it has been found that noise can induce chaos and destruct the zero Lyapunov exponent in the situation where a nonchaotic attractor coexists with a nonattracting chaotic saddle, as in a periodic window [Phys. Rev. Lett. 88 (2002) 124101]. Here we report that noise can also destruct the zero Lyapunov exponent in coupled chaotic systems where there is only one attractor. Moreover, the zero Lyapunov exponent in noise free will become positive when adding noise and be proportional to the average frequency of bursting induced by noise. A physical theory and numerical simulations are presented to explain how the average frequency of bursting depends on the coupling and noise strength.     

The largest Lyapunov exponent of chaotic dynamical system in scale space and its application

The largest Lyapunov exponent is an important invariant of detecting and characterizing chaos produced from a dynamical system. We have found analytically that the largest Lyapunov exponent of the small-scale wavelet transform modulus of a dynamical system is the same as the system's largest Lyapunov exponent, both discrete map and continuous chaotic attractor with one or two positive Lyapunov exponents. This property has been used to estimate the largest Lyapunov exponent of chaotic time series with several kinds of strong additive noise.     

Chaos Control for Coupling of the Double-Well Duffing System Based on Random Phase Disturbance

Non-feedback methods of chaos control are suited for practical applications. For possible practical applications of the control methods, the robustness of the methods in the presence of noise is of special interest. The noise can be in the form of external disturbances to the system or in the form of uncertainties due to inexact model of the system. This paper deals with the effect of random phase disturbance for a class of coupling of the Double-Well Duffing system in the presence of the noise. Lyapunov index is an important indicator to describe chaos. When the sign of the top Lyapunov exponent is positive, the system is chaotic. We compute top Lyapunov exponent by the Khasminskii’s transform formula of spherical coordinate and extension of Wedig’s algorithm based on linear stochastic system. With the change of the average of top Lyapunov exponent sign, we show that random phase can suppress chaos. Finally Poincaré map and phase portraits analysis are studied to confirm the obtained results.     

Sensitivity to initial conditions and lyapunov exponent of a quasiperiodic system

The dynamics of a quasiperiodic map is analyzed both in the presence and in the absence of weak noise. It is shown that, in the presence of weak noise, a strange chaotic attractor with a negative Lyapunov exponent and sensitive dependence of trajectories on the initial conditions can exist in the system. This means that the types of motion of a fluctuating system cannot be classified only by the sign of the leading Lyapunov exponent.     

舰船辐射噪声超混沌现象研究

水下弱目标探测和识别一直是水声信号处理领域中研究的难点。从Lyapunov指数谱、吸引子相空间轨迹的演化、分形维数等方面，对船舶辐射噪声是否存在超混沌现象进行了研究。实验结果表明，船舶辐射噪声信号确实存在至少两个正的Lyapunov指数，即存在超混沌现象。辐射噪声吸引子在相空间中的轨迹具有多方向伸展的趋势，且不同类型目标的吸引子具有不同的分形维数。研究结果为建立精确描述辐射噪声信号的非线性模型、为水下弱目标信号探测和识别提供一定的理论依据。     

基于最大Lyapunov指数不变性的混沌时间序列噪声水平估计

提出了一种基于最大Lyapunov指数不变性的计算混沌时间序列噪声水平的新方法. 首先分析了噪声对相空间中两点距离的影响, 然后基于最大Lyapunov指数在不同维数的嵌入相空间不变的性质, 建立了估计噪声水平的方法. 仿真计算结果表明, 当噪声水平小于10% 时, 估计值与真实值符合良好. 该方法对噪声分布类型不敏感, 是一种有效的混沌时间序列噪声估计方法.     

Shot noise in chaotic systems: "classical" to quantum crossover

This paper is devoted to study of the classical-to-quantum crossover of the shot noise in chaotic systems. This crossover is determined by the ratio of the particle dwell time in the system, tau(d), to the characteristic time for diffraction t(E) approximately lambda(-1)|lnh, where lambda is the Lyapunov exponent. The shot noise vanishes when t(E)>tau(d), while it reaches a universal value in the opposite limit. Thus, the Lyapunov exponent of chaotic mesoscopic systems may be found by shot noise measurements.     

基于小波变换的光混沌信号消噪与Lyapunov指数计算

针对动力学方程未知且信噪比小的光混沌信号,采用小波多分辨分解算法对其进行噪音消减.用Lorenz混沌信号对该算法的消噪效果进行了检验.提出利用互信息量法和Cao氏法来改进小数据量法在时间延迟和嵌入维数计算上存在的主观选择性,对经过噪音消减的Lorenz混沌信号利用此改进的小数据量法计算其最大Lyapunov指数.结果表明,信噪比可提高近10 dB左右,最大Lyapunov指数计算误差可减少近30%,并求得半导体放大器光混沌信号的最大Lyapunov指数为0.389 6.     

Fluctuations and simple chaotic dynamics

We describe the effects of fluctuations on the period-doubling bifurcation to chaos. We study the dynamics of maps of the interval in the absence of noise and numerically verify the scaling behavior of the Lyapunov characteristic exponent near the transition to chaos. As previously shown, fluctuations produce a gap in the period-doubling bifurcation sequence. We show that this implies a scaling behavior for the chaotic threshold and determine the associated critical exponent. By considering fluctuations as a disordering field on the deterministic dynamics, we obtain scaling relations between various critical exponents relating the effect of noise on the Lyapunov characteristic exponent. A rule is developed to explain the effects of additive noise at fixed parameter value from the deterministic dynamics at nearby parameter values.     

Synchronization of periodic self-oscillations by common noise

We consider the effect of external noise on the stability properties of self-oscillations. A stochastic equation for the phase is derived at the limit of weak noise (in the appropriate sense). The stationary probability-density distribution is used for an analytic calculation of the Lyapunov exponent. We show that the exponent is always negative for the small noise level, which corresponds to synchronization of self-oscillations.     

Effect of noise on the neutral direction of chaotic attractor

A chaotic attractor from a deterministic flow must necessarily possess a neutral direction, as characterized by a null Lyapunov exponent. We show that for a wide class of chaotic attractors, particularly those having multiple scrolls in the phase space, the existence of the neutral direction can be extremely fragile in the sense that it is typically destroyed by noise of arbitrarily small amplitude. A universal scaling law quantifying the increase of the Lyapunov exponent with noise is obtained. A way to observe the scaling law in experiments is suggested.     

Synchronization of self-sustained oscillators by common white noise

We study the stability of self-sustained oscillations under the influence of external noise. For small-noise amplitude a phase approximation for the Langevin dynamics is valid. A stationary distribution of the phase is used for an analytic calculation of the maximal Lyapunov exponent. We demonstrate that for small noise the exponent is negative, which corresponds to synchronization of oscillators.     

Experimental characterization of transition to chaos in the presence of noise

Transition to chaos in the presence of noise is an important problem in nonlinear and statistical physics. Recently, a scaling law has been theoretically predicted which relates the Lyapunov exponent to the noise variation near the transition. Here we present experimental observation of noise-induced chaos in an electronic circuit and obtain the fundamental scaling law characterizing the transition. The experimentally obtained scaling exponent agrees very well with that predicted by theory.     

Lyapunov exponent of the random Schrödinger operator with short-range correlated noise potential

We study the influence of disorder on propagation of waves in one-dimensional structures. Transmission properties of the process governed by the Schrödinger equation with the white noise potential can be expressed through the Lyapunov exponent γ which we determine explicitly as a function of the noise intensity σ and the frequency ω. We find uniform two-parameter asymptotic expressions for γ which allow us to evaluate γ for different relations between σ and ω. The value of the Lyapunov exponent is also obtained in the case of a short-range correlated noise, which is shown to be less than its white noise counterpart.     

Renormalization group analysis of some dynamical systems with noise

We formulate a renormalization group analysis for the study of the accumulation of period doubling in the presence of noise. The main tool is a renormalization of the time evolution of the noise. The critical indices depend on the nature of the noise, but are given by thermodynamic quantities describing the large deviations of the Lyapunov exponent of the linearized random renormalization.     

A stochastic epidemic model on homogeneous networks

In this paper, a stochastic SIS epidemic model on homogeneous networks is considered. The largest Lyapunov exponent is calculated by Oseledec multiplicative ergodic theory, and the stability condition is determined by the largest Lyapunov exponent. The probability density function for the proportion of infected individuals is found explicitly, and the stochastic bifurcation is analysed by a probability density function. In particular, the new basic reproductive number R*, that governs whether an epidemic with few initial infections can become an endemic or not, is determined by noise intensity. In the homogeneous networks, despite of the basic productive number R₀>1, the epidemic will die out as long as noise intensity satisfies a certain condition.     

New prediction of chaotic time series based on local Lyapunov exponent

A new method of predicting chaotic time series is presented based on a local Lyapunov exponent, by quantitatively measuring the exponential rate of separation or attraction of two infinitely close trajectories in state space. After reconstructing state space from one-dimensional chaotic time series, neighboring multiple-state vectors of the predicting point are selected to deduce the prediction formula by using the definition of the local Lyapunov exponent. Numerical simulations are carried out to test its effectiveness and verify its higher precision over two older methods. The effects of the number of referential state vectors and added noise on forecasting accuracy are also studied numerically.