An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

基于深腾1800机群系统的分子动力学并行仿真研究

介绍了分子动力学并行仿真计算的软硬件环境,分析了现有的几种并行算法,确定采用区域分解法作为并行算法,并在此基础上提出了基于区域二次划分的分子动力学并行仿真算法.另外,阐述了原子链、原子近邻表和原子亲属表的概念,提出了基于永久序号的消息传递策略.最后,设计了分子动力学并行仿真程序,并分别在1、2、3、4台结点机上进行了实验,运行结果表明:加速比随着结点数的增加而增加,并行效率虽略有下降但都在87.5%以上,并行效率并没有随着结点数的增加有明显的降低,说明并行程序具有很好的扩展性.     

基于BEMD-Copula-GARCH模型的股票投资组合VaR风险度量研究

鉴于股票波动具有显著的多尺度特征,本文引入二元经验模态分解(EMD)与二元CopulaGARCH算法,提出一种新的VaR风险度量模型,即BEMD-Copula-GARCH模型.具体地,新BEMD-Copula-GARCH模型可分为三个主要步骤:数据分析,分风险估计和总风险集成.首先,基于二元EMD模型,将复杂且相互作用的股票对分解为若干组较为简单且相互独立的分量,以降低建模难度.其次,引入二元Copula-GARCH模型,刻画各组分量间的相互关系,以度量股票投资组合在不同尺度上的分VaR值.最后,集成各分VaR值以得出最终VaR风险度量结果.实证研究以恒生指数与上证综指为数据样本构造投资组合,结果表明:本文所构建的新模型能有效度量投资组合风险,其估计精度显著优于DCC-GARCH和Copula-GARCH等现有模型.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

一个计算行星轮对定轴动量矩的新方法

推导了行星轮的运动分解为随转动参考系的转动和相对于转动参考系的转动时,计算其对定轴动量矩的一个新方法.该方法表明,行星轮对定轴动量矩等于牵连动量矩与相对动量矩之和,其物理意义直观明确,应用方便.     

单样本条件下基于图像增强和Fourier频谱的人脸识别

目前有许多处理正面视觉人脸的识别方法,当有充分数量的有代表性的样本时,能取得较好的识别效果。然而当处理单样本识别问题时,现有的许多方法的识别率将明显下降或甚至不适用。为了加强单训练样本的分类信息,训练样本与其基于受扰动的奇异值的重构图组合成新样本,Fourier频谱作为人脸识别特征,在ORL人脸库上的实验结果表明了该方法的有效性。     

广义系统多传感器分布式融合降阶Kalman滤波器

对于带多传感器的广义线性离散随机系统,应用奇异值分解,将其变换为等价的两个降阶多传感器子系统,提出了基于变换后的状态融合器构造原始状态融合器的新的融合方法。应用Kalman滤波方法,在线性最小方差按矩阵加权、按对角阵加权和按标量加权融合准则下,分别提出了三种最优加权融合降阶广义Kalman滤波器。可统一处理融合滤波、平滑和预报问题。可减少计算负担和改善局部滤波精度。证明了三种融合器和局部估值器之间的精度关系。为了计算最优加权。提出了局部滤波误差协方差阵的计算公式。一个Monte Carlo仿真例子说明了其有效性。     

纳米Ag（Ⅰ，Ⅲ）O粉体化学沉淀法制备条件的正交试验及其光学热分解性能研究

本文首先利用正交试验确定了液相沉淀法合成纳米AgO粉体的最佳制备条件。接着研究了最佳条件下制备的纳米AgO粉体的性能。UV—vis DRS分析结果表明，反射率随着AgO含量的增加而降低，高纯度AgO在400nm-1100nm波长范围内具有较强的吸收能力。TG—DTG分析结果表明，在N2气氛下，AgO热分解为Ag2O。的温度范围为100℃—200℃，Ag2O热分解为Ag的温度范围为400℃-450℃。     

热传导方程的一类区域分解差分算法

把求解区域分成若干个子域,在不同子域中采用不同的计算步长,对方程的紧差分格式在特殊情形下采用区域分解法,并给出相应的先验误差估计式。