ID 原文 译文
16895 针对城市短时交通流序列非线性和混沌性的特点，为提高短时交通流的预测精度，该文提出一种基于多维时空的非线性主成分分析(NPCA)和相空间重构(PSR)的改进灰色(IGM(1,1))组合预测模型。 In view of the nonlinear and chaos of urban short-term traffic flow sequence, this article proposes acombined prediction model based on multi-dimensional spatio-temporal Nonlinear Principal ComponentAnalysis (NPCA) and Phase Space Reconstructed (PSR) Improved Gray Model (IGM(1,1)) in order to improveits forecast accuracy.
16896 首先，使用数据相关性的非线性主成分分析算法对多维交通流量序列进行时空降维，同时保留影响预测点的主要交通流量数据，从而提高建模的精确度； First, the data correlation NPCA algorithm is used to reduce the spatial and temporal dimensions of multi-dimensional traffic flow sequences, while preserving the main traffic flow data that affects the predicted points, so as to improve the accuracy of the modeling.
16897 其次，利用多维时空交通流量序列相空间重构放大交通流量内部的细微特征，以使其内在规律得以充分展现，进一步提升预测精度； Phase space reconstruction amplifies the subtle features inside the traffic flow, so that its internal laws can be fully displayed, and improve further the prediction accuracy.
16898 最后，结合背景值改进的灰色模型适应于线性、非线性以及所需数据少的特点，进行短时交通流预测。 Finally, the gray model combined with the improved background value is adapted to thecharacteristics of linearity, non-linearity and less required data.
16899 实验结果表明，NPCA-PSR-IGM(1,1)组合预测模型的平均相对误差相比NPCA-PSR-GM(1,1)组合预测模型减小3.12%，其标准偏差相对PCA-PSR-IGM(1,1)组合预测模型从15.7091下降到2.0589。 Short-term traffic flow is predicted. Theexperimental results show that the average relative error of the NPCA-PSR-IGM (1,1) combination predictionmodel is 3.12% smaller than that of the NPCA-PSR-GM (1,1) combination prediction model, and its standarddeviation is relative to the PCA-PSR-IGM (1,1) combination prediction model has dropped from 15.7091 to2.0589.
16900 同时与最新的预测模型相比，该组合预测模型也提高了预测精度，达到了较好的预测效果。 At the same time, compared with the latest prediction model, the combined prediction model alsoimproves the prediction accuracy and achieves a better prediction effect.
16901 在解决射频识别(RFID)标签天线设计中阻抗计算速度慢的问题的过程中，针对其中较为复杂的阻抗耦合情况，该文提出一种基于多项式的弯折偶极子RFID标签天线阻抗预测方法。 In the process of solving the problem of slow impedance calculation speed in the design of Radio Frequency IDentification (RFID) tag antenna, a method of impedance prediction based on polynomial for folded dipole RFID tag antenna is proposed in view of the complex impedance coupling.
16902 首先使用基于天线尺寸的阻抗变换与线性化假设建立模型假设；然后在具体的天线结构中收集数据并进行相关性分析与回归拟合验证假设正确性； Firstly, impedancetransformation based on antenna size and linearization assumption are used to establish model hypothesis.Then the data are collected from the antenna structure and the correlation analysis and regression fitting arecarried out to verify the validity of the hypothesis.
16903 最后实验验证使用该方法进行的阻抗预测相对于计算机仿真的准确性、高效性与普适性。 Finally, the accuracy, efficiency and universality of theimpedance prediction method compared with computer simulation are verified by experiments.
16904 试验结果表明，使用该方法替代计算机进行弯折偶极子RFID标签天线阻抗计算时，其预测阻抗相对于计算机仿真结果在保持较高预测准确率的同时极大地缩短了阻抗计算时间，同时该方法在中国应用频段上针对不同弯折次数的弯折偶极子RFID标签天线仍然适用。 Theexperimental results show that the predicted impedance can greatly shorten the calculation time whilemaintaining high prediction accuracy when the proposed method replace computer to calculate the impedanceof the folded dipole RFID tag antenna, and the method is still applicable to the RFID tag antenna withdifferent bending times in the frequency band used in China.