基于非线性预测效果的癫痫脑电信号的特征提取方法

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.     

EAST�����п������HALO������ģ�����

基于EAST超导托卡马克实验装置,给出了HALO电流计算方法和计算过程,并通过自由能方法模拟得出了等离子体电流、HALO电流在等离子体破裂过程中随时间的演化过程。     

HALO���������µ�EAST������غɷ���

在弄清产生HALO电流的物理机制的基础上,分析了EAST超导托卡马克装置的最大HALO电流及其引起的最大电磁载荷,并采用数值模拟方法,对真空室结构在HALO电流作用下的受力情况进行了分析。     

Analyses of Optimal Embedding Dimension and Delay for Local Linear Prediction Model

In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space reconstruction. We propose a criterion based on prediction error to determine the optimal parameters of the proposed model. The simulation results show that the proposed model can effectively make one-step and multistep prediction for chaotic time series, and the one-step and multi-step prediction accuracy of the proposed model is superior to that of the traditional local linear prediction.     

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.     

A new method of determining the optimal embedding dimension based on nonlinear prediction

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.     

基于团划分的成像侦察任务聚类方法研究

成像侦察任务聚类是提高成像侦察卫星利用效率的重要手段。在分析成像侦察任务间聚类关系的基础上,建立了成像侦察任务聚类模型,采用图论中的团划分理论对其进行求解,并给出了相应算法,最后用一个示例验证了算法的有效性。     

Small-time scale network traffic prediction based on a local support vector machine regression model

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.     

大学物理基础实验教学现状的调查分析

对大学物理基础性实验的教学现状做了初步的问卷调查,显示出很多学生对实验重视程度不够、缺乏兴趣、对教师依赖性大以及实验教学方式单一等,从而针对性地提出了一些教学建议.     

基于信息准则的局域预测法邻近点的选取方法

基于信息准则,提出了选取局域预测法中邻近点个数的定量方法. 并用此方法分析Lorenz模型生成的混沌时间序列和Santa Fe时间序列竞赛的激光数据(Data A). 实验结果表明用该方法选取邻近点的局域预测法的一步和多步预测性能较好,在满足预测精度较高的条件下,计算量较小. 关键词： 邻近点 邻域 局域预测 信息准则