基于局域相关向量机回归模型的小尺度网络流量的非线性预测

基于非线性时间序列局域预测法与相关向量机回归模型, 本文提出了局域相关向量机预测方法, 并应用于预测实际的小尺度网路流量序列. 应用基于信息准则的局域预测法邻近点的选取方法来选取局域相关向量机回归模型的邻近点个数. 对比分析了局域相关向量机预测法、前馈神经网络模型与局域线性预测法对网络流量序列的预测性能, 其中前馈神经网络模型的参数采用粒子群优化算法来优化. 实验结果表明: 邻近点优化后的局域相关向量机回归模型能够有效地预测小尺度网络流量序列, 归一化均方误差很小; 局域相关向量机回归模型生成的时间序列具有与原网络流量时间序列相一致的概率分布; 局域相关向量机回归模型的预测精度好于前馈神经网络模型的与局域线性预测法的. 关键词： 小尺度网络流量 非线性时间序列预测方法 局域预测法 相关向量机回归模型     

基于最大Lyapunov指数的多变量混沌时间序列预测

参考基于最大Lyapunov指数的单变量混沌时间序列预测方法,提出一种通过选取多个邻近重构向量,预测多变量混沌时间序列的局域法.采用新方法对两个完全不同的Rssler方程的耦合系统,Rssler方程和Hyper Rssler方程的耦合系统的多变量混沌时序进行一步和多步预测,结果表明了该方法的有效性,且算法具有较强的抗噪能力.讨论了参考邻近点数和预测结果的关系. 关键词： Lyapunov指数 混沌时间序列预测 多变量时间序列 最小二乘法     

混沌时间序列多步自适应预测方法

针对混沌时间序列局域自适应预测方法在多步预测中预测器系数无法调节的问题，根据混沌时间序列的短期可预测性及自适应算法的自适应跟踪混沌运动轨迹的特点，提出了混沌时间序列多步自适应预测方法.仿真结果表明，此方法的多步预测性能明显好于局域自适应预测方法的多步预测性能. 关键词： 多步自适应预测方法 局域自适应预测方法 混沌时间序列     

时空混沌序列的局域支持向量机预测

结合局域预测法计算速度快的优点和支持向量机的泛化性能好、全局最优、稀疏解等特性，用局域支持向量机预测研究了时空混沌序列的局域预测性能，并用局域支持向量机预测模型讨论了嵌入维数、邻近个数选择以及时空混沌的耦合方式和格子间的耦合强度变化对时空混沌局域预测性能的影响.研究结果表明：局域支持向量机不仅比全局支持向量机、局域零阶预测、局域线性预测等方法具有更好的预测性能，且具有对嵌入维数和邻近个数不敏感的优点；时空混沌的耦合方式和格子间的耦合强度对时空混沌序列的预测性能有明显影响.     

风电功率时间序列混沌特性分析及预测模型研究

为揭示风电功率序列内在的动态特性, 利用非线性方法对风电时间序列混沌特性进行识别, 为对风电功率进行预测提供了基础.首先对某风电场的风电功率时间序列的日相关性进行了分析;然后在相空间重构的基础上计算了风电序列的最大Lyapunov指数, 验证了风电时间序列的混沌特性;由于采用Volterra滤波器多步预测法对风电功率进行超短期预测误差较大, 利用局域多步预测法以及最大Lyapunov指数法的预测结果并结合加权马尔科夫链和有序算子对Volterra滤波器的预测结果进行校正.最后以某实际风电场的风电功率预测为算例, 仿真结果表明校正预测模型有效的提高了预测精度, 其为利用Volterra滤波器多步法进行风电预测提供了有益的参考.     

短时交通流复杂动力学特性分析及预测

为揭示短时交通流的内在动态特性,利用非线性方法对交通流混沌特性进行识别,为短时交通流的预测提供基础.基于混沌理论对交通流时间序列进行相空间重构,利用C-C算法计算时间延迟和嵌入维数,采用Grassberger-Procaccia算法计算吸引子关联维数,通过改进小数据量法计算最大Lyapunov指数来判别交通流时间序列的混沌特性.针对局域自适应预测方法在交通流多步预测中预测器系数无法调节的问题,提出了交通流多步自适应预测方法.通过实测数据计算,结果表明:2,4和5 min三种统计尺度的交通流时间序列均具有混沌特性;改进的小数据量法能够准确地计算出最大Lyapunov指数;构建的交通流多步自适应预测模型能够有效地预测交通流量的变化.为智能交通系统诱导和控制提供了依据.     

基于多元局部多项式方法的混沌时间序列预测

根据Takens定理，把混沌时间序列构造为一组序列对，然后用多元局部多项式方法来预测其序列.这种核估计方法可以结合局域法与全局法的优点，使得预测的精度更高.仿真结果表明，该方法非常有效.     

基于时间序列预测的电子稳像算法研究

块匹配电子稳像算法是一种稳定性好、准确度高的电子稳像算法.块匹配算法在目标区域中从起始点到匹配点进行搜索时,需要对图像块进行反复匹配,计算量大、实时性差成为限制其应用的主要问题.本文从缩小块匹配算法搜索范围的思想出发,提出了一种利用时间序列预测来确定最优搜索起始点的电子稳像算法.根据图像序列全局运动矢量的内部统计特性,选择合适的时间序列模型;采用AIC准则和Durbin-Levinson递推算法估计模型的阶次和参量,并通过残差检验对模型进行检验和更新.利用建立的时间序列模型和历史数据对当前时刻全局运动矢量进行最优预测,并将其作为搜索起点来进行下一步精确搜索.实验结果证明,时间序列预测方法有效缩小了块匹配算法的搜索范围,使计算速度得到较大幅度的提高,并可直接推广到其它电子稳像算法中.     

混沌时间序列的一种局域离散余弦变换域Volterra预测

提出了一种局域离散余弦变换（DCT）域Volterra预测，并用于混沌时间序列预测。DCT被用来减少Volterra预测器的矩阵计算复杂性。数值仿真结果表明：本文提出的方法比传统的局域线性预测方法能更有效地预测混沌时间序列和预测精度。     

基于多变量混沌时间序列的煤矿斜井TBM施工动态风险预测

在全面分析煤矿长斜井TBM(盾构)施工动态风险特点的基础上,利用多变量混沌时间序列预测方法对其进行预测.利用主成分分析法,确定影响煤矿长斜井TBM施工风险的主要成分.对煤矿长斜井TBM施工风险多变量时间序列进行相空间的重构,确定时间延迟τi和嵌入维数mi,采用小数据量法计算煤矿长斜井TBM施工多变量风险时间序列的最大Lyapunov指数,证明了其具有混沌特性,提出了一阶局域法与双隐层神经网络的组合预测模型,该模型能够对多变量风险时间序列随时间的变化进行预测.仿真实验表明,该预测模型误差小于单变量时间序列的预测误差,具有较强的预测能力和较好的预测效果,可为煤矿长斜井TBM施工风险分析与评估提供一种新的途径.     

A new local linear prediction model for chaotic time series

A new local linear prediction model is proposed to predict chaotic time series in this Letter. We propose that the parameters—the embedding dimension and the time delay of the local linear prediction model—can take values which are different to those of the state space reconstruction in the procedure of finding the nearest neighbor points. We propose a criterion based on prediction power to determine the optimal parameters of the new local linear prediction model. Simulation results show that the new local linear prediction model can effectively predict chaotic time series and the prediction performance of the new local linear prediction model is superior to that of the local linear prediction.     

Time Series Prediction Based on Chaotic Attractor

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.     

基于遗传注意力机制的DLSTM电力系统混沌预测

提出一种基于遗传算法优化注意力机制的深度长短期记忆网络(DLSTM)方法,用于电力系统的混沌预测。通过传递共享参数,将遗传算法优化的注意力机制加入DLSTM模型中,可以挖掘时间序列中潜在特征,同时避免陷入局部优化。该方法是一种受进化计算方法启发的寻优方法,可以很好地学习注意力层中的参数。电力系统混沌预测实验表明所提模型比其他参考模型具有更高的预测精度和长期预测能力。     

Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.     

A derivative based simplified phase tracker for a single fringe pattern demodulation

In this paper, a novel fringe demodulation method for the estimation of phase and its first-order derivative from a closed-fringe interferogram is proposed. The proposed method determines the phase derivatives in both x&y directions from fringe orientation and density. The phase derivatives are subsequently used to determine phase values using a novel simplified phase tracker. In the phase tracking model, the complexity of the cost function is reduced using predetermined derivatives so computation time required for phase tracking is reduced considerably. The proposed model is more robust while dealing with saddle points in fringes than the conventional phase tracker model. Hence it does not require any specialized scanning strategy. The proposed method is validated with simulated and experimental fringe patterns (obtained using electronic speckle pattern interferometry and optical holographic interferometry) and a comparison study is carried out with conventional regularized phase tracker. The simulation results show that the proposed method has good accuracy and requires less computation time than existing phase-tracking algorithms. The experimental results demonstrate the robustness of the proposed method against speckle noise and its practical applicability for static and dynamic applications.     

Characterization of a wavelength selection method using near-infrared spectroscopy and partial least squares with false nearest neighbors and its application in the detection of the chemical oxygen demand of waste liquid

Abstract

The spectral wavelength selection method is important in near-infrared spectroscopy. Eliminating redundant information and extracting useful information can improve the prediction accuracy and modeling efficiency of the quantitative analysis model for spectral analysis to obtain a near-infrared calibration model with strong predictability and good robustness. This paper proposes a wavelength selection method for near-infrared spectroscopy by combining the partial least squares and false nearest neighbor methods. In this method, the correlation between the characteristic wavelength variables and the measured index is assessed by means of a similarity-based distance measure of the characteristic wavelength variable, and the characteristic wavelength is selected according to the order of the correlation. The method was used to select characteristic wavelengths from the near-infrared spectrum of waste liquid to establish a prediction model for the chemical oxygen demand. Compared with the full-spectrum partial least squares and interval partial least squares based models, the number of characteristic wavelength variables is reduced from 1557 to 176, and the prediction accuracy of the model is improved. This method both simplifies the model and achieves higher prediction accuracy. Therefore, this study provides a novel solution for wavelength selection for multivariate calibration in near-infrared spectroscopy.     

基于改进确定采样型滤波的疲劳裂纹扩展预测

针对确定性采样滤波在进行状态估计预测时随维数增加时出现计算量增加且精度不高的问题,提出一种确定采样型滤波的算法并将其应用到疲劳裂纹扩展预测当中去。首先,阐述了确定采样型滤波器的基本原理;其次,从多维数值积分的角度出发,引入完全对称积分公式,根据不变容积法则选取容积节点作为基础采样点,计算积分节点、节点个数和权重;最后,将改进后的确定采样型滤波器应用到构件疲劳裂纹损伤扩展中去,并与无迹卡尔曼滤波算法、容积卡尔曼滤波算法进行比较,提升了裂纹扩展预测的精度,仿真分析验证了该方法的可行性和有效性。     

基于LTS-HD的像素跳跃式快速景象匹配算法

在分析Hausdorff距离特性的基础上,提出了一种两级实时景象匹配算法.与传统各种利用图像多尺度特征的多级匹配方法不同,该算法利用Hausdorff 距离特性直接在原分辨率图像上进行匹配,通过“减少匹配位置”以及“减少匹配位置相似性测度计算量”两种途径缩短匹配时间.跳跃式搜索极大地减少了参与匹配的位置数;而在每个匹配位置,只计算由特征点组成的两个点集间的LTS-HD相似性测度,非特征点不参与计算,从而大大减少了该匹配位置的相似性测度计算量.为了保证匹配准确度,采用由粗到精的两级匹配策略,第一级采用像素跳跃式全局搜索获得粗匹配点,第二级以第一级匹配为基础,在以粗匹配点为中心的δ邻域内局部遍历搜索获得精匹配点.仿真分析表明,提出的算法相比传统的遍历搜索及遗传算法耗时短且定位准确,在实时图存在严重遮挡的情况下仍能正确匹配.