首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到18条相似文献,搜索用时 93 毫秒
1.
Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it ,the paper pressents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Fimally,some validation examples and results are given.  相似文献   

2.
孟庆芳  彭玉华  薛佩军 《中国物理》2007,16(5):1252-1257
A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.  相似文献   

3.
李恒超  张家树 《中国物理快报》2005,22(11):2776-2779
Based on phase space delay-coordinate reconstruction of a chaotic dynamics system, we propose a local prediction of chaotic time series using a support vector machine (SVM) to overcome the shortcomings of traditional local prediction methods. The simulation results show that the performance of this proposed predictor for making onestep and multi-step prediction is superior to that of the traditional local linear prediction method and global SVM method. In addition, it is significant that its prediction performance is insensitive to the selection of embedding dimension and the number of nearest neighbours, so the satisfying results can be achieved even if we do not know the optimal embedding dimension and how to select the number of nearest neighbours.  相似文献   

4.
Time Series Prediction Based on Chaotic Attractor   总被引:1,自引:0,他引:1  
A new prediction technique is proposed for chaotic time series. The usefulness of the technique is that it can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. A time-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the time evolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate the local dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is tested for the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this prediction technique can improve the prediction of chaotic time series.  相似文献   

5.
王俊松  袁静  李强  袁睿翕 《中国物理 B》2011,20(5):50506-050506
This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP,FTP and SMTP.First,the phase space is reconstructed and the embedding parameters are obtained by the mutual information method.Secondly,the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature,i.e.HTTP and FTP traffics are chaotic,furthermore,the former is more complex than the later;on the other hand,SMTP traffic is stochastic.It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP,FTP and SMTP protocol network traffics.This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.  相似文献   

6.
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   

7.
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.  相似文献   

8.
孙建成  张太镒  刘枫 《中国物理》2004,13(12):2045-2052
Positive Lyapunov exponents cause the errors in modelling of the chaotic time series to grow exponentially. In this paper, we propose the modified version of the support vector machines (SVM) to deal with this problem. Based on recurrent least squares support vector machines (RLS-SVM), we introduce a weighted term to the cost function tocompensate the prediction errors resulting from the positive global Lyapunov exponents. To demonstrate the effectiveness of our algorithm, we use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Santa Fe competition time series. The simulation results shows that the proposed method can capture the dynamics of the chaotic time series effectively.  相似文献   

9.
10.
A new prediction technique is proposed for chaotic time series.The usefulness of the technique is that it removes some false neighbouring points which are not suitable for the local estimation of the dynamics systems.We use a feedforward neural network to approximate the local dominant Lyapunov exponent,and choose the neighbouring points by the exponent.The model is tested for the convection amplitude of the Lorenz model,and the results indicate that this prediction technique can improve the prediction of chaotic time series.  相似文献   

11.
李鹤  杨周  张义民  闻邦椿 《物理学报》2011,60(7):70512-070512
根据Takens定理,研究了混沌时间序列相空间重构嵌入维数的选取问题.提出了基于径向基函数神经网络预测模型性能的嵌入维数估计方法,即根据嵌入维数与混沌时间序列预测模型性能的变化关系来确定嵌入维数.通过对几种典型混沌动力学系统的数值验证,结果表明该方法能够确定出合适的相空间重构嵌入维数. 关键词: 混沌 相空间重构 嵌入维数 预测  相似文献   

12.
We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variable’s trajectory. The reduced models are nonlinear autoregression (NAR) time series models, with coefficients estimated from data by least squares. The NAR models can accurately reproduce the energy spectrum, the invariant densities, and the autocorrelations. Taking advantage of the simplicity of the NAR models, we investigate maximal space-time reduction. Reduction in space dimension is unlimited, and NAR models with two Fourier modes can perform well. The NAR model’s stability limits time reduction, with a maximal time step smaller than that of the K-mode Galerkin system. We report a potential criterion for optimal space-time reduction: the NAR models achieve minimal relative error in the energy spectrum at the time step, where the K-mode Galerkin system’s mean Courant–Friedrichs–Lewy (CFL) number agrees with that of the full model.  相似文献   

13.
混沌时间序列的支持向量机预测   总被引:43,自引:0,他引:43       下载免费PDF全文
崔万照  朱长纯  保文星  刘君华 《物理学报》2004,53(10):3303-3310
根据混沌动力系统的相空间延迟坐标重构理论,基于支持向量机的强大的非线性映射能力, 建立了混沌时间序列的支持向量机预测模型,并在统计学习理论的基础上采用最小二乘方法来训练预测模型,利用该模型对嵌入维数与模型的均方根误差的关系进行了探讨.最后利用Mackey-Glass时间序列和变参数的Ikeda 时间序列对该模型进行了验证,结果表明,该预测模型能精确地预测混沌时间序列,而且在混沌时间序列的嵌入维数未知时也能取得比较好的预测效果.这一结论预示着支持向量机是一种研究混沌时间序列的有效方法. 关键词: 混沌时间序列 支持向量机 最小二乘法  相似文献   

14.
张玉梅  胡小俊  吴晓军  白树林  路纲 《物理学报》2015,64(20):200507-200507
对给定的英语音素、单词和语句进行了采集并完成预处理. 分别应用互信息法和Cao 氏法确定了实际采集的语音信号序列的延迟时间和嵌入维数, 以完成语音序列的相空间重构. 通过计算实际采集的语音信号序列的最大Lyapunov指数, 完成了语音信号的混沌特性识别, 判定其具有混沌特性. 引入Volterra级数, 提出了一种具有显式结构的语音信号非线性预测模型. 为克服最小均方误差算法在Volterra模型系数更新时固有的缺点, 在最小二乘法基础上, 应用基于后验误差假设的可变收敛因子技术, 构建了一种基于Davidon-Fletcher-Powell算法的二阶Volterra 模型(DFPSOVF), 并将其应用于具有混沌特性的语音信号序列预测. 仿真结果表明: DFPSOVF非线性预测模型对于单帧和多帧语音信号均具有更好的预测精度, 优于线性预测模型, 并且能够很好地反映语音序列变化的趋势和规律, 完全可以满足语音预测的要求; 可以根据语音信号序列的嵌入维数选取预测模型的记忆长度. 所提出模型可以为语音信号重构和压缩编码开辟一条新途径, 以改善语音信号处理方法的复杂度和处理效果.  相似文献   

15.
在非线性时间序列预测研究的基础上,提出了基于非线性预测效果的癫痫脑电信号特征提取方法,从脑电信号中自动检测出癫痫脑电信号.采用基于可预测性的选取嵌入维数的方法确定脑电信号序列的嵌入维数,进行相空间重构.实验结果表明:基于非线性预测效果的特征提取方法提取的特征能明显地区分癫痫脑电信号与正常脑电信号,该非线性特征提取方法适合小数据量的情况且对噪声的稳定性好.  相似文献   

16.
In this work correlation integrals are used for the analysis of various EEG signals from rabbits in resting states and under the influence of an anesthetic. The comparison with surrogate data reveals nonlinear dynamics in all of the time series. Our attempt to determine the correlation dimension D(2) by the modified algorithm of Theiler [Phys. Rev. A 34, 2427 (1986)] failed since no saturation is reached with increasing embedding dimension. The hypothesis of low-dimensional chaos turns out to be inconsistent with our results, but we can still distinguish, at least qualitatively, between different states of brain dynamics. A quantitative characterization of the time series is possible by defining correlation parameters P(a) derived from correlation integrals reflecting also autocorrelation of the signal. (c) 1996 American Institute of Physics.  相似文献   

17.
基于模糊模型支持向量机的混沌时间序列预测   总被引:7,自引:0,他引:7       下载免费PDF全文
基于支持向量机强大的非线性映射能力和模糊逻辑易于将先验的系统知识结合到模糊规则的 特性, 根据混沌动力系统的相空间重构理论, 提出了一种混沌时间序列的模糊模型的支持向 量机预测模型,并采用适用于大规模问题求解的最小二乘法来训练预测模型,利用该模型分别 对模型的整体预测性能与嵌入维数及延迟时间的关系进行了探讨.最后利用Mackey-Glass时 间序列和典型的Lorenz系统生成的时间序列对该模型进行了验证,结果表明该预测模型不仅 能够自动的从学习数据中获取知识产生模糊规则,提取能够代表混沌时间序列内在规律的支 持向量,大大减少支持向量的数目,精确地预测未来的混沌时间序列,而且在混沌时间序列 的嵌入维数未知和延迟时间不能合理选择的情况下,也能取得比较好的预测效果.这一结论预 示着基于模糊模型的支持向量机是一种研究混沌时间序列的有效方法. 关键词: 模糊模型 混沌时间序列 支持向量机 最小二乘法  相似文献   

18.
张春涛  马千里  彭宏 《物理学报》2010,59(11):7623-7629
提出一种混沌时间序列相空间重构参数的信息熵优化方法(IEOP),该方法首先使用条件熵表示信息量,建立时间延迟和嵌入维数在相空间中的信息熵优化模型,然后利用遗传算法同时求解两个重构参数,使重构坐标间既保持了良好的独立性又保留了原系统的动力学特征.通过在Lorenz和Mackey-Glass系统上的数值实验,该方法不仅能够确定合适的嵌入维数和时间延迟,而且能在优化的相空间中获得更多的信息,提高了混沌时间序列的预测精度.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号