皮层脑电的非线性降噪

引入基于对非线性动力学局部线性拟合的局部投影非线性降噪方法对Spragure-Dawley大鼠的皮层脑电进行降噪.为了提高降噪效果，利用返回图法对皮层脑电降噪时所需要的最佳局部邻域尺度进行了估计.首先以被50%的高斯白噪声污染的Lorenz方程x轴为例进行降噪，说明根据降噪理论所编写的计算程序的正确性.然后将此降噪方法分别应用于被麻醉的大鼠的皮层脑电和青霉素溶液诱发癫痫发作的皮层脑电时间序列，并采用非线性预报分析说明降噪的效果. 关键词： 皮层脑电 返回图法 非线性降噪 非线性预报     

基于二阶离散变分方法的非线性映射参数估计

提出一种估计非线性映射未知参数的二阶离散变分方法.首先针对非线性离散混沌系统, 利用变分方法导出了伴随方程和目标泛函梯度, 以此为基础利用二阶离散变分方法给出了二阶伴随方程和精确计算Hessian矩阵-向量乘积的显式表达式; 其次设计了估计非线性映射未知参数的新算法, 并以此对Hyperhenón映射和二维抛物映射中的未知参数进行了精确的估计. 数值仿真结果表明了该方法的有效性和优点. 关键词： 非线性映射 参数估计 二阶离散变分方法 伴随方程     

汉语语音的非线性动力学特征及其降噪应用

分析了汉语语音的相关维、最小嵌入维数以及重构相图。分析结果表明汉语语音具有混沌特征。根据这些非线性特征可以有效区分汉语中的浊音、清音和随机噪声,从而可以用于语音降噪。介绍了本地投影法混沌语音降噪的原理与算法,并利用该算法对一些典型的元音和辅音进行降噪,获得了较好的降噪效果。     

受污染混沌信号的协同滤波降噪

根据混沌吸引子的自相似分形特性,提出了一种利用协同滤波重构受污染混沌信号的降噪算法.所设计的降噪算法通过对相似片段的分组将一维混沌信号的降噪转化为一个二维联合滤波问题;然后,在二维变换域用阈值法衰减噪声;最后,通过反变换获得原始信号的估计.由于分组中的相似片段具有良好的相关性,与直接在一维变换域做阈值降噪相比,分组的二维变换能获得原信号更稀疏的表示,更好地抑制噪声.仿真结果表明,该算法对原始混沌信号的重构精度和信噪比的提升都优于小波阈值、局部曲线拟合等现有的混沌信号降噪方法,对相图的还原质量也更好.     

非线性动力系统分岔图中的暗线

基于非线性动力系统混沌运动的回归特性，构造了一种对分岔图中穿过混沌区的暗线进行研究的数值回归算法。运用该算法求得抛物线映射的暗线，并与通过暗线方程精确求得的暗线进行比较，验证了算法的有效性。对Brussel振子系统和分段线性单级齿轮动力系统的暗线进行了研究。通过对非线性动力系统分岔图中暗线的研究，由其切点可以得到嵌在混沌区中的周期窗口，由其交点可以得到混沌吸引子的激变点。研究结果表明该算法有助于分析系统的动力学行为和控制混沌运动。     

基于快速全线性预测控制的混沌系统控制与同步

针对连续时间混沌(超混沌)系统的控制问题, 提出了一种基于扩张状态观测器的快速全线性广义预测控制算法. 利用线性扩张状态观测器估计和补偿混沌(超混沌)系统的非线性动力学和存在的不确定性, 将原始对象近似转化为积分器形式, 随后针对单积分器设计广义预测控制, 解决了预测控制计算量大的问题. 阶跃系数矩阵可以直接得到解析解, 而对于未来输出的预测则可以根据最近两个时刻的输出采样值直接计算得到, 避免了使用自校正算法和在线求解丢番图方程. 该线性算法可以直接应用于非线性对象的控制系统设计. 将该算法应用于典型Lorenz混沌系统的控制中, 数学仿真结果验证了有效性.     

流场非线性特征提取与混沌分析

为解决传统方法在判断离心压气机动态失稳过程中因信号强非线性导致误判错判,针对其动态时序属非线性信号,基于分形理论提出自适应变分模态分解(adaptive variational mode decomposition with fractal,AFVMD)方法以同时实现降噪与非线性特征提取,采用相空间重构法还原系统动力学结构.以某离心压气机失稳过程中叶轮动态压力数据为对象,验证所提出算法的优越性,分析其吸引子状态.结果表明:在处理具有非线性特征的含噪信号时, AFVMD比小波降噪具有更好的降噪效果与特征提取能力;相空间将失速发展过程可视化,最小流量状态所对应的相空间呈现"毛球状";随失速的发展,相空间将逐渐发散;经小波与AFVMD方法预处理的信号所对应相形对失速过程更加敏感;通过经AFVMD处理的信号进行重构可更早捕获失速征兆,其更小的最大Lyapunov指数表明该方法提升了流动混沌系统的可预测性,为压气机失稳分析、预测提供新思路与方法.     

基于变分方法的混沌系统参数估计

提出一种基于变分原理的估计混沌系统未知参数的方法,对以x= F(x,θ) 为控制方程的所有混沌系统具有普适性.首先将混沌系统方程引入到目标泛函中;接着利用变分原理导出了混沌系统的伴随方程和待辨识参数泛函梯度的通用公式;然后设计了估计混沌系统未知参数的算法;最后对典型的Lorenz混沌系统和超混沌Chen系统的未知参数进行了估计.数值仿真结果表明该方法是一种非常有效的估计混沌系统未知参数的方法. 关键词： 混沌系统 参数估计 变分方法 伴随方程     

基于斑点噪声的拖尾Rayleigh分布的合成孔径雷达图像最大后验概率降噪

为了反映合成孔径雷达图像中斑点噪声尖峰厚尾的统计特征，使用拖尾Rayleigh分布来描述斑点噪声.基于Gamma先验分布和斑点噪声的拖尾Rayleigh分布，推导出了合成孔径雷达图像的最大后验概率滤波方程，并给出了它在特定特征参数时的解析形式.使用Mellin变换从观察图像估计拖尾Rayleigh分布的未知参数.给出了在斑点噪声的拖尾Rayleigh分布下的最大后验概率降噪试验和量化指标.为了消除滑动窗大小和噪声强度对降噪结果的影响，给出了降噪能力随滑动窗大小和噪声方差的动态变化关系.结果表明，拖尾Ray 关键词： 斑点噪声 拖尾Rayleigh分布 最大后验概率降噪 Mellin变换     

两级式光伏并网逆变器建模与非线性动力学行为研究

本文首先建立了两级式光伏并网逆变器严格的分段光滑状态方程, 分析级联情况下光伏阵列电压对光伏并网逆变器非线性动力学行为的影响, 然后探讨拓展两级式光伏并网逆变器输入电压范围的策略, 并研究前后级电路内部参数变化引起并网逆变器输出电流的快变尺度分岔和慢变尺度分岔现象. 研究发现: 若对光伏阵列电压进行分段控制, 可以有效展宽两级式光伏并网逆变器的输入电压范围; 适当增加前级输出电容值、电感量, 可以避免系统产生混沌运动, 而后级参数的取值需避开多个不连续的混沌区域. 研究结果对提高光伏发电系统的效率与稳定性有较重要的参考价值.     

Noise reduction by recycling dynamically coupled time series

We say that several scalar time series are dynamically coupled if they record the values of measurements of the state variables of the same smooth dynamical system. We show that much of the information lost due to measurement noise in a target time series can be recovered with a noise reduction algorithm by crossing the time series with another time series with which it is dynamically coupled. The method is particularly useful for reduction of measurement noise in short length time series with high uncertainties.     

基于搜索平均法的气象观测数据的非线性去噪

介绍了一种新型基于相空间重构和返回图法的非线性降噪方法——搜索平均法，首先将该方法应用于被高斯白噪声所污染的Henon映射时间序列，说明根据降噪理论所编写的计算程序的正确性；然后应用于中国气象局公布全国435站1960—2000年的逐日气温观测时间序列，并利用非线性预报方法衡量降噪的效果，验证了该方法的有效性.     

Geometric noise reduction for multivariate time series

We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.     

滑动移除小波分析法在动力学结构突变检验中的应用

标度指数计算的即时性与准确性对相关时间序列的动力学结构突变分析至关重要,然而现有方法在即时性与准确性上一直无法兼顾.将小波分析方法与滑动移除窗口技术相融合,提出一种新的动力学结构突变检测方法——滑动移除小波分析法.通过选取不同的滑动移除窗口,分别对构建的线性、非线性理想时间序列进行动力学结构突变分析,结果表明不论是线性时间序列还是非线性时间序列,滑动移除小波分析能够准确地检测到序列的动力学结构突变点及突变区间,对于滑动移除窗口长度依赖性较小,具有很强的稳定性,而且在计算速度上明显优于滑动移除重标极差和滑动移除方差分析方法,将在大数据处理中具有一定的优势.同时分别对线性、非线性理想时间序列添加高斯白噪声,结果表明滑动移除小波分析具有很强的抗噪能力,能够准确地检测到加噪后序列的突变点.对佛坪站日最高温度实测资料的动力学结构突变的准确检测进一步验证了该方法的有效性.滑动移除小波分析法可为具有相关性的系统动力学结构突变的快速、准确检测提供一种途径.     

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.     

Volatility dynamics of wavelet-filtered stock prices

Nontrivial mixture of long-range correlations and noise is one of the characteristic features of the dynamics of complex systems. Filtering of noise in such systems presents a difficult challenge. In the present paper this problem is studied by the example of volatility dynamics of wavelet-filtered stock price time series. Using the universal thresholding method of wavelet filtering and a principle of minimal linear autocorrelation of noise component we find that the quantitative characteristics of long-range memory in the volatility dynamics of denoised series are noticeably different from those of the raw data and the noise.     

Error Covariance Matrix Estimation of Noisy and Dynamically Coupled Time Series

We estimate the covariance matrix of the errors in several dynamically coupled time series corrupted by measurement errors. We say that several scalar time series are dynamically coupled if they record the values of measurements of the state variables of the same smooth dynamical system. The estimation of the covariance matrix of the errors is made using a noise reduction algorithm that efficiently exploits the information contained jointly in the dynamically coupled noisy time series. The method is particularly powerful for short length time series with high uncertainties.     

An efficient method of distinguishing chaos from noise

It is an important problem in chaos theory whether an observed irregular signal is deterministic chaotic or stochastic. We propose an efficient method for distinguishing deterministic chaotic from stochastic time series for short scalar time series. We first investigate, with the increase of the embedding dimension, the changing trend of the distance between two points which stay close in phase space. And then, we obtain the differences between Gaussian white noise and deterministic chaotic time series underlying this method. Finally, numerical experiments are presented to testify the validity and robustness of the method. Simulation results indicate that our method can distinguish deterministic chaotic from stochastic time series effectively even when the data are short and contaminated.     

The properties and mechanism of long-term memory in nonparametric volatility

Recent empirical literature documents the presence of long-term memory in return volatility. But the mechanism of the existence of long-term memory is still unclear. In this paper, we investigate the origin and properties of long-term memory with nonparametric volatility, using high-frequency time series data of the Chinese Shanghai Composite Stock Price Index. We perform Detrended Fluctuation Analysis (DFA) on three different nonparametric volatility estimators with different sampling frequencies. For the same volatility series, the Hurst exponents reduce as the sampling time interval increases, but they are still larger than 1/2, which means that no matter how the interval changes, it still cannot change the existence of long memory. RRV presents a relatively stable property on long-term memory and is less influenced by sampling frequency. RV and RBV have some evolutionary trends depending on time intervals, which indicating that the jump component has no significant impact on the long-term memory property. This suggests that the presence of long-term memory in nonparametric volatility can be contributed to the integrated variance component. Considering the impact of microstructure noise, RBV and RRV still present long-term memory under various time intervals. We can infer that the presence of long-term memory in realized volatility is not affected by market microstructure noise. Our findings imply that the long-term memory phenomenon is an inherent characteristic of the data generating process, not a result of microstructure noise or volatility clustering.