EGFR基因C端结构域真核表达载体的构建

构建EGFR基因C端结构域的真核表达载体.应用PCR技术,从含EGFR基因C端结构域的大肠杆菌DH 5α中扩增其序列,亚克隆到真核表达载体pcDNA3.1( )中,经酶切及测序进行验证.PCR扩增片段与预期结果相符,真核表达载体构建成功,测序结果与GenBank公布的基因一致.成功地构建了EGFR基因C端两个结构域的真核表达载体.     

复空间形式的紧致全实伪脐子流形

主要研究复空间形式中具有平行法平均曲率向量的紧致全实伪脐子流形,给出了截面曲率作为全脐子流形判定条件的结果.     

变频防爆绞车电控装置的研制

在分析防爆绞车现状的基础上,阐明变频绞车电控装置的必要性。提出了一种基于空间矢量的PWM(Pu lseW idth M odu lation)算法的全数字化变频器,对空间矢量脉宽调制的理论进行了简要的介绍。该变频器主要由主回路和控制电路两部分组成。控制电路采用数字信号处理器TM S320LF2407以硬件的形式实现SVPWM(Space V ector Pu lse W idthM odu lation)波的输出;主回路中采用智能功率模块DYNEX作为功率器件。经在500 kW交流电机上实验,结果令人满意。     

基于贝叶斯学习的关联向量机及其在软测量中的应用

介绍了一种与支持向量机(SVM)函数形式相同的稀疏概率模型--关联向量机(RVM),其训练是在贝叶斯框架下进行的,在处理具有噪声的函数回归时,RVM具有很出色的性能.与SVM相比不仅解更稀疏,而且无需调整模型参数,核函数选择也不受限制.将RVM应用于PTA装置溶剂脱水塔塔顶塔底组分软测量建模,仿真结果表明:该方法预测精度较高,具有一定的应用价值.     

基于核函数主元分析的SVM建模方法及应用

为有效克服线性建模方法在非线性建模方面的不足,将核函数思想引入到主元分析方法(PCA)中,有效提取实验数据中的非线性特征信息,并将其作为支持向量机(SVM)的输入变量,建立工业过程软测量模型。该方法应用于丙烯腈聚合过程中转化率的预报,结果表明:该方法的预测精度优于PCA-SVM方法和KPCA-NN方法。     

支持向量机的最大间隔和对偶性

最初出现的支持向量机理论是基于2类线性可分问题的.针对线性可分情况,研究表明线性硬间隔分类机的对偶问题与凸壳问题(平分最近点法)是等价的,线性硬间隔分类机的最大间隔与凸壳问题的2个最近点的距离相等:针对非线性可分情况,研究表明线性软间隔分类机的对偶问题与缩小的凸壳问题(推广的平分最近点法)是等价的,线性软间隔分类机的最大间隔与缩小的凸壳问题的2个最近点的距离相等.对支持向量机分类问题给出了直观解释.     

基于双相移组合全相位法的FIR陷波器设计

提出新的双相移组合全相位设计FIR陷波器算法。根据单窗情况下全相位滤波的3个重要性质,利用两个互为对称的陷波频率向量用全相位方法设计出两个子滤波器,并构造两个互为共轭的相移向量分别对两个子滤波器进行相移,再将两者进行组合即可生成陷波器。通过理论证明:陷波器的设计可通过代入一简单公式来实现,计算复杂度非常低。实验表明:通过改变相移参数值,在任意频率点上陷波器的衰减值可达-330dB。     

局部变权的公理体系

提出了局部变权的公理化定义,研究了与之相应的局部状态变权,得到了两类基本均衡函数.最后,作为原理的应用,给出了一个例子.     

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.     

Beating the VAR: Improving Swedish GDP Forecasts Using Error and Intercept Corrections

This paper examines the forecast accuracy of an unrestricted vector autoregressive (VAR) model for GDP, relative to a comparable vector error correction model (VECM) that recognizes that the data are characterized by co‐integration. In addition, an alternative forecast method, intercept correction, is considered for further comparison. Recursive out‐of‐sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VECM consistently outperforms the VAR models. Further, intercept correction enhances the forecast accuracy when applied to the VECM, whereas there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the intercept corrected VECM compared to the VAR model. Copyright © 2015 John Wiley & Sons, Ltd.