Analytical Fitting Functions of Finite Sample Discrete Entropies of White Gaussian Noise

In order to find the convergence rate of finite sample discrete entropies of a white Gaussian noise(WGN), Brown entropy algorithm is numerically tested.With the increase of sample size, the curves of these finite sample discrete entropies are asymptotically close to their theoretical values.The confidence intervals of the sample Brown entropy are narrower than those of the sample discrete entropy calculated from its differential entropy, which is valid only in the case of a small sample size of WGN. The differences between sample Brown entropies and their theoretical values are fitted by two rational functions exactly, and the revised Brown entropies are more efficient. The application to the prediction of wind speed indicates that the variances of resampled time series increase almost exponentially with the increase of resampling period.

GAUSSIAN WHITE NOISE CALCULUS OF GENERALIZED EXPANSION

A new framework of Gaussian white noise calculus is established, in line with generalized expansion in [3, 4, 7]. A suitable frame of Fock expansion is presented on Gaussian generalized expansion functionals being introduced here, which provides the integral kernel operator decomposition of the second quantization of Koopman operators for chaotic dynamical systems, in terms of annihilation operators partial derivative(t) and its dual, creation operators partial derivative(t)*.

Stochastic stability of FitzHugh-Nagumo systems perturbed by Gaussian white noise

The current paper is devoted to the study of the stochastic stability of FitzHugh-Nagumo systems perturbed by Gaussian white noise. First, the dynamics of stochastic FitzHugh-Nagumo systems are studied. Then, the existence and uniqueness of their invariant measures, which mix exponentially are proved. Finally, the asymptotic behaviors of invariant measures when size of noise gets to zero are investigated.

Some Applications of Higher Moments of the Linear Gaussian White Noise Process

The Linear Gaussian white noise process is an independent and identically distributed (iid) sequence with zero mean and finite variance with distribution N (0, σ2 ) . Hence, if X1, x2, …, Xn is a realization of such an iid sequence, this paper studies in detail the covariance structure of X1d, X2d, …, Xnd, d=1, 2, …. By this study, it is shown that: 1) all powers of a Linear Gaussian White Noise Process are iid but, not normally distributed and 2) the higher moments (variance and kurtosis) of Xtd, d=2, 3, … can be used to distinguish between the Linear Gaussian white noise process and other processes with similar covariance structure.

Global Pressure of One-Dimensional Polydisperse Granular Gases Driven by Gaussian White Noise

We study the global pressure of a one-dimensional polydisperse granular gases system for the first time, in which the size distribution of particles has the fractal characteristic and the inhomogeneity is described by a fractal dimension D. The particles are driven by Gaussian white noise and subject to inelastic mutual collisions. We define the global pressure P of the system as the impulse transferred across a surface in a unit of time, which has two contributions, one from the translational motion of particles and the other from the collisions. Explicit expression for the global pressure in the steady state is derived. By molecular dynamics simulations, we investigate how the inelasticity of collisions and the inhomogeneity of the particles influence the global pressure. The simulation results indicate that the restitution coefficient e and the fractal dimension D have significant effect on the pressure.

Characteristic Analysis of White Gaussian Noise in S-Transformation Domain

The characteristic property of white Gaussian noise (WGN) is derived in S-transformation domain. The results show that the distribution of normalized S-spectrum of WGN follows X2?distribution with two degrees of freedom. The conclusion has been confirmed through both theoretical derivations and numerical simulations. Combined with different criteria, an effective signal detection in S-transformation can be realized.

THE REGULARIZED SOLUTION APPROXIMATION OF FORWARD/BACKWARD PROBLEMS FOR A FRACTIONAL PSEUDO-PARABOLIC EQUATION WITH RANDOM NOISE

This paper deals with the forward and backward problems for the nonlinear fractional pseudo-parabolic equation ut+(-Δ)^(s1)ut+β(-Δ)^(s2)u=F(u,x,t)subject o random Gaussian white noise for initial and final data.Under the suitable assumptions s1,s2andβ,we first show the ill-posedness of mild solutions for forward and backward problems in the sense of Hadamard,which are mainly driven by random noise.Moreover,we propose the Fourier truncation method for stabilizing the above ill-posed problems.We derive an error estimate between the exact solution and its regularized solution in an E‖·‖Hs22norm,and give some numerical examples illustrating the effect of above method.

A MODEL FOR WHITE NOISE ANALYSIS IN P-ADIC NUMBER FIELDS

We propose a simple model for white noise analysis in the p-adic case based on the p-adic analogue of the space L(2)(R(infinity), d mu).

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

In this paper,a reinforced gradient-type iterative learning control pro file is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise.The robustness is analyzed and the range of the step is speci fied by means of statistical technique and matrix theory.Compared with the conventional one,the proposed algorithm is more ef ficient to resist external noise.Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

Effect of Gaussian Noise Amplitude on Translation Error Estimated Using Double-Wayland Algorithm

The Wayland algorithm has been improved in order to evaluate the degree of visible determinism for dynamical systems that generate time series. The objective of this study is to show that the Double-Wayland algorithm can distinguish between time series generated by a deterministic process and those generated by a stochastic process. The authors conducted numerical analysis of the van der Pol equation and a stochastic differential equation as a deterministic process and a Ganssian stochastic process, respectively. In case of large S/N ratios, the noise term did not affect the translation error derived from time series data, but affected that from the temporal differences of time series. In case of larger noise amplitudes, the translation error from the differences was calculated to be approximately 1 using the Double-Wayland algorithm, and it did not vary in magnitude. Furthermore, the translation error derived from the differenced sequences was considered stable against noise. This novel algorithm was applied to the detection of anomalous signals in some fields of engineering, such as the analysis of railway systems and bio-signals.

Recovery of Transient Signals in Noise by OptimalThresholding in Wavelet Domain

：研究用离散子波变换复原被加性高斯白噪声污染的瞬态信号．在子波域中提出了一种最佳门限方法，该方法涉及到对于波系数的假设检验，利用似然比、奈曼，皮尔逊准则和最小均方差设计该门限，计算机仿真证明，该方法在较低信噪比下复原信号的有效性．

NOISE-INDUCED CHAOTIC MOTIONS IN HAMILTONIAN SYSTEMS WITH SLOW-VARYING PARAMETERS

This paper studies chaotic motions in quasi-integrable Hamiltonian systems with slow-varying parameters under both harmonic and noise excitations. Based on the dynamic theory and some assumptions of excited noises, an extended form of the stochastic Melnikov method is presented. Using this extended method, the homoclinic bifurcations and chaotic behavior of a nonlinear Hamiltonian system with weak feed-back control under both harmonic and Gaussian white noise excitations are analyzed in detail. It is shown that the addition of stochastic excitations can make the parameter threshold value for the occurrence of chaotic motions vary in a wider region. Therefore, chaotic motions may arise easily in the system. By the Monte-Carlo method, the numerical results for the time-history and the maximum Lyapunov exponents of an example system are finally given to illustrate that the presented method is effective.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

Noise-Induced Transition in a Voltage-Controlled Oscillator Neuron Model

In the presence of Gaussian white noise,we study the properties of voltage-controlled oscillator neuronmodel and discuss the effects of the additive and multiplicative noise.It is found that the additive noise can accelerate andcounterwork the firing of neuron,which depends on the value of central frequency of neuron itself,while multiplicativenoise can induce the continuous change or mutation of membrane potential.

MAXIMUM LIKELIHOOD SOURCE SEPARATION FOR FINITE IMPULSE RESPONSE MULTIPLE INPUT-MULTIPLE OUTPUT CHANNELS IN THE PRESENCE OF ADDITIVE NOISE

Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.

变截面压电悬臂梁能量采集器在高斯白噪声激励下的稳态统计特性研究

环境振动是一种非周期、随机的宽频激励,研究振动能量采集器在环境振动下的能量采集特性具有重要意义。本文采用改进随机平均法,求解了变截面压电梁在高斯白噪声激励下的等效振幅、位移、速度的稳态概率密度函数,位移与速度的联合概率密度函数以及稳态均方输出电压,随后研究了变截面压电梁的能量采集效能。结果表明:负载电阻一定时,截面系数β>0时的变截面压电梁相比β=0时的等截面压电梁有更好的稳态均方输出电压;截面系数β>0时,随着电阻电容乘积的倒数的增加,变截面压电梁的均方电压均呈现逐渐减小的趋势,当电阻电容乘积的倒数值一定时,β值越大,均方电压也越高;随着噪声强度的增加,变截面压电梁的均方电压均呈现逐渐增大的趋势,当噪声强度一定时,β值越大,均方电压也越高。本文研究结果可为变截面压电悬臂梁能量采集系统的设计及应用提供理论依据。

基于遗传算法的高速铁路桥梁损伤识别

针对高速铁路桥梁结构服役局部损伤问题,文章提出一种改进损伤识别因子并结合遗传算法的损伤识别方法。推导了任意单元长度组合的车辆-轨道-桥梁耦合单元及其动力方程,遗传算法采用锦标赛策略及最优保存策略2种选择算子,引入灾变及邻域搜索机制。通过对不同损伤工况进行多次独立重复计算,对算法的稳定性与准确性进行统计分析。结果表明:在信噪比(signal-to-noise ratio,SNR)大于25 dB时,识别效果好,极少数误判的结果大概率存在于真实损伤位置的附近或对称处;与单个单元损伤情况相比,多个单元损伤工况平均第一次出现最优解的迭代次数略高,但整体都偏低;SNR与识别率(identification rate,IR)呈“扁S”型Logistic曲线关系。

CWNT时频尺度多步噪声抑制的轴承故障诊断

实际工况中的轴承信号常带有不同程度的噪声干扰,不利于识别轴承的健康状态,且严重影响其故障诊断的稳定性。针对噪声信号的处理难题,提出了一种CWNT时频尺度多步噪声抑制的轴承故障诊断方法。通过不同时频尺度分解,利用连续小波变换(CWT)的带通特性,可变长度时窗截取变换定位含噪信号的集中波形;从全局感受野对故障信号滤波,深层卷积非线性提取时频图像特征;在通道维度增加注意力机制,加权归一化权重至每个通道的特征,拟合通道间的复杂关联,以此达到多步噪声抑制的诊断效果。为验证所提诊断方法,向凯斯西储大学轴承数据集(CWRU)添加功率比为4.987%、12.538%、31.650%的高斯白噪声,诊断得到验证集准确率分别为97.384%、96.701%、95.407%。结果表明,CWNT方法具有较强的抗噪能力,能够提高噪声背景下故障诊断的准确性。

基于ACBO-GM联合方法的高维变量结构模型修正

工程结构模型修正常面临变量维度高、非线性程度强等现实问题,其模型修正的精度和效率受到严重影响,为改善这一不足,提出基于改进物体碰撞算法(Advanced Colliding Bodies Optimization,ACBO)和高斯白噪声扰动(Gaussian-white-noise Mutation,GM)的联合修正方法。在联合方法中,ACBO实现从传统的“一对一”到“优对多”的碰撞模式的转变,GM则用于保证碰撞过程中种群的多样性,可有效提升结构模型修正效率。基于一系列测试函数,对比分析标准物体碰撞算法(Colliding Bodies Optimization,CBO)和联合优化方法的优化性能;给出基于ACBO-GM联合方法的修正流程,并将其应用于钢框架修正算例和悬臂梁修正实例,对比研究基于联合修正方法与基于遗传算法(Genetic Algorithm,GA)的修正精度和效率。研究表明,ACBO-GM联合优化方法在精度和效率方面都明显优于传统的CBO方法,该优化方法的可行性得到验证;基于ACBO-GM联合方法在钢框架和悬臂梁两个工程结构修正案例中的应用,其修正精度均与基于GA方法的精度一致,但修正效率显著优于基于GA的修正方法。

非高斯波浪作用下深水高墩的非线性随机振动

首先,建立非高斯波浪作用下深水高墩的随机动力学模型,采用泊松白噪声激励模拟非高斯随机波浪过程,利用达朗贝尔原理和伽辽金方法推导深水高墩的运动方程。然后,通过径向基神经网络法求解广义FPK方程,获得系统的瞬态响应概率密度函数。最后,考察不同结构参数对系统响应的影响,并采用蒙特卡罗模拟(MCS)验证理论解。结果表明:理论解与模拟结果吻合良好;浸入比和质量比增加均会放大高墩的响应;采用高斯模型会使结构设计偏于保守。