共查询到20条相似文献,搜索用时 156 毫秒
1.
为了快速和实时地从具有强噪声的较低信噪比的原始信号中检测出有用信息,设计了一种混沌相空间重构理论和ELMAN神经网络的信号检测方法;首先,描述了采用混沌相空间重构理论对原始信号进行重构的原理和方法,在获取重构的时间序列的基础上,采用ELMAN网络来近似表示用于检测信号的函数型,然后,设计了ELMAN网络中各层之间连接权值的计算方式,并提出了采用ELMAN网络进行信号检测的具体过程,最后给出了采用混沌相空间重构理论和ELMAN网络的信号检测模型;对Lorenz混沌系统模型进行仿真实验,结果证明了文章方法能有效地对瞬时信号和周期性信息进行检测,在具有高斯白噪声的情况下,仍然具有降噪效果好的优点,是一种用于信号检测的可行性方法。 相似文献
2.
强混沌背景中的微弱谐波信号检测有重要的工程研究意义. 目前的检测方法主要是基于Takens理论的混沌相空间重构方法, 然而这些方法往往对信干噪比要求高, 且对高斯白噪声敏感等. 本文注意到混沌信号的二阶统计特性是不变的, 根据这个特点提出了一种基于最优滤波器的强混沌背景中的微弱谐波信号检测方法. 该方法首先构建一个数据矩阵, 在频域上对每个频率通道分别检测谐波信号, 从而将信号检测问题转化为最优化问题, 然后利用最优化理论设计滤波器, 使待检测频率通道的信号增益保持不变, 而尽量抑制其他频率通道的信号, 最后通过判断每一频率通道的输出信干噪比来检测谐波信号. 与传统方法相比, 本文方法有如下优点: 1)可以检测更低信干噪比下的微弱谐波信号; 2)可检测的信号幅度范围更大; 3)抗白噪声性能更强. 仿真结果证明了本文方法的有效性. 相似文献
3.
4.
基于Takens的相空间延迟坐标重构,研究了用于混沌信号预测的三阶Volterra滤波器的一种乘积耦合近似实现结构,并应用于典型的低维混沌时间序列和具有高维混沌特性的EEG信号的预测.数值研究表明:这种滤波器结构对于低维混沌时间序列的预测精度可以比二阶Volterra滤波器提高103倍,而且能够较好地对一些具有高维混沌特性的EEG信号进行预测
关键词:
混沌
非线性自适应预测
三阶Volterra滤波器
electroencephalography信号 相似文献
5.
本文提出了一种新的混沌时间序列高维相空间多元图重心轨迹动力学特征提取方法. 在确定了最佳嵌入维数和延迟时间后, 将相空间中高维矢量点映射到二维平面的雷达图上, 相应地将相空间中高维矢量点变换为对应的几何多边形. 通过提取几何多边形的重心位置得到重心轨迹动力学演化特性, 并利用重心轨迹矩特征量区分不同性质的混沌时间序列. 在此基础上, 处理分析了气液两相流电导传感器动态信号, 发现高维相空间多元图重心轨迹矩特征量不仅可以辨识泡状流、段塞流和混状流, 而且为流型动力学演化机理提供了新的分析途径. 相似文献
6.
7.
8.
螺旋桨鸣音的混沌动力特性研究 总被引:2,自引:0,他引:2
利用混沌动力学方法研究螺旋桨鸣音信号时间序列,估计时间序列的相空间重构最佳参数,并提出其具有混沌动力特性,分析了系统拓扑维数的边界和生成系统所必须独立变量的个数,还计算分析了重构相空间中吸引子轨迹随时间演化的发散情况。分析计算结果表明:螺旋桨鸣音信号时间序列可以选取最佳延迟时间tD=1、最小嵌入维数dE=8进行相空间重构,其混沌吸引子的关联维数为5.1579、最大Lyapunov指数为0.0771,此研究结果可以为螺旋桨鸣音现象的进一步研究提供理论基础。 相似文献
9.
10.
为了从混沌背景中检测微弱信号,研究分析了复杂非线性系统的相空间重构理论,提出了一种基于广义窗函数的最小二乘支持向量机的预测法. 该方法以广义嵌入窗为基础,利用自关联函数法确定Lorenz系统的嵌入维数和时间延迟, 实现相空间重构,结合最小二乘支持向量机建立Lorenz系统的误差预测模型, 检测微弱目标信号(瞬态和周期信号).仿真实验表明,该方法的预测模型具有较小的误差, 能够有效地从混沌背景噪声中检测出微弱目标信号,减小噪声对目标信号的影响. 与传统方法相比,在降低检测门限的同时,能够有效地提高预测的精度, 在混沌噪声下信噪比为-87.41 dB的情况下,相对于传统支持向量机方法所得的均方根误差0.049(-54.60 dB时)降低近两个数量级至0.000036123(-87.41 dB时). 相似文献
11.
混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性. 相似文献
12.
Rescaled Range Permutation Entropy: A Method for Quantifying the Dynamical Complexity of Extreme Volatility in Chaotic Time Series 下载免费PDF全文
《中国物理快报》2020,(9)
Information entropy,as a quantitative measure of complexity in nonlinear systems,has been widely researched in a variety of contexts.With the development of a nonlinear dynamic,the entropy is faced with severe challenges in dealing with those signals exhibiting extreme volatility.In order to address this problem of weighted permutation entropy,which may result in the inaccurate estimation of extreme volatility,we propose a rescaled range permutation entropy,which selects the ratio of range and standard deviation as the weight of different fragments in the time series,thereby effectively extracting the maximum volatility.By analyzing typical nonlinear systems,we investigate the sensitivities of four methods in chaotic time series where extreme volatility occurs.Compared with sample entropy,fuzzy entropy,and weighted permutation entropy,this rescaled range permutation entropy leads to a significant discernibility,which provides a new method for distinguishing the complexity of nonlinear systems with extreme volatility. 相似文献
13.
Application of permutation entropy method in the analysis of chaotic,noisy, and chaotic noisy series
We have considered a permutation entropy method for analyzing chaotic, noisy, and chaotic noisy series. We have introduced the concept of permutation entropy from a survey of some features of information entropy (Shannon entropy), described the algorithm for its calculation, and indicated the advantages of this approach in the analysis of time series; the application of this method in the analysis of various model systems and experimental data has also been demonstrated. 相似文献
14.
通过在互耦合垂直腔面发射激光器(VCSELs)系统中增加外光注入, 建立了一种基于偏振可调光反馈VCSEL驱动互耦合VCSELs混沌系统模型, 分析了增加外光驱动对互耦合激光器随机特性的影响. 以不可预测度作为随机特性的评价指标, 采用信息论中的排列熵作为相应量化工具, 对系统输出混沌信号的不可预测性进行定量分析.数值研究了光强度、时延、偏振旋转角度以及驱动激光器与耦合激光器间的频率失谐对输出信号随机特性的影响.结果表明: 外光注入能够增大互耦合VCSELs输出混沌信号的排列熵, 即外光注入能够有效提高耦合系统的随机特性; 驱动激光器可调偏振片偏转角度调节到45° 附近, 注入强度适中, 满足耦合强度大于驱动激光器自反馈强度条件, 系统输出信号的排列熵较大; 在耦合时延与驱动激光器反馈时延不相等的同时, 增加驱动激光器与耦合激光器频率失谐, 外光注入互耦合VCSELs的随机特性能够得到进一步提高. 相似文献
15.
16.
乙醇含量拉曼光谱检测中,拉曼光谱信号中的各种噪声及光谱荧光造成的基线漂移和样品池背景等,影响了校正模型的预测精度。利用总体平均经验模态分解,将光谱信号分解成若干无模态混叠的内在模式分量,根据排列熵的信号随机性检测判据判断出代表背景信息和噪声信息的内在模式分量,将其置零即可同时消除拉曼光谱中的噪声与背景。将总体平均经验模态分解与排列熵相结合的预处理方法应用于乙醇含量的拉曼光谱检测中,并与小波变换和平均平滑滤波做了对比。实验结果表明:应用总体平均经验模态分解与排列熵相结合的方法能够有效的同时消除乙醇含量拉曼光谱检测中的噪声和背景信息,提高校正模型的预测精度,且使用简便,无需参数设置,对乙醇含量拉曼光谱检测具有实用价值。 相似文献
17.
Identification of deterministic chaos by an information-theoretic measure of the sensitive dependence on the initial conditions 总被引:1,自引:0,他引:1
One of the most difficult problems in the field of non-linear time series analysis is the unequivocal characterization of a measured signal. We present a practicable procedure which allows to decide if a given time series is pure noise, chaotic but distorted by noise, purely chaotic, or a Markov process. Furthermore, the method gives an estimate of the Kolmogorov-Sinai (KS) entropy and the noise level. The procedure is based on a measure of the sensitive dependence on the initial conditions which is called ε-information flow. This measure generalizes the concept of KS entropy and characterizes the underlying dynamics. The ε-information flow is approximated by the calculation of various correlation integrals. 相似文献
18.
An attempt is made in this study to estimate the noise level present in a chaotic time series. This is achieved by employing a linear least-squares method that is based on the correlation integral form obtained by Diks in 1999. The effectiveness of the method is demonstrated using five artificial chaotic time series, the Henon map, the Lorenz equation, the Duffing equation, the Rossler equation and the Chua's circuit whose dynamical characteristics are known a priori. Different levels of noise are added to the artificial chaotic time series and the estimated results indicate good performance of the proposed method. Finally, the proposed method is applied to estimate the noise level present in some real world data sets. 相似文献
19.
A universal algorithm to generate pseudo-random numbers based on uniform mapping as homeomorphism 下载免费PDF全文
A specific uniform map is constructed as a homeomorphism mapping chaotic time series into [0,1] to obtain sequences of standard uniform distribution. With the uniform map, a chaotic orbit and a sequence orbit obtained are topologically equivalent to each other so the map can preserve the most dynamic properties of chaotic systems such as permutation entropy. Based on the uniform map, a universal algorithm to generate pseudo random numbers is proposed and the pseudo random series is tested to follow the standard 0-1 random distribution both theoretically and experimentally. The algorithm is not complex, which does not impose high requirement on computer hard ware and thus computation speed is fast. The method not only extends the parameter spaces but also avoids the drawback of small function space caused by constraints on chaotic maps used to generate pseudo random numbers. The algorithm can be applied to any chaotic system and can produce pseudo random sequence of high quality, thus can be a good universal pseudo random number generator. 相似文献
20.
Phase space reconstruction is the first step to recognizing
the chaos from observed time series. On the basis of differential
entropy, this paper introduces an efficient method to estimate the
embedding dimension and the time delay simultaneously. The
differential entropy is used to characterize the disorder degree of
the reconstructed attractor. The minimum value of the differential
entropy corresponds to the optimum set of the reconstructed
parameters. Simulated experiments show that the original phase space
can be effectively reconstructed from time series, and the
accuracy of the invariants in phase space reconstruction is greatly
improved. It provides a new method for the identification of chaotic
signals from time series. 相似文献