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   时间特征尺度 的翻译结果: 查询用时:0.213秒
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时间特征尺度
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  time characteristic scale
     And based on the concept of time characteristic scale, a new approach to border processing named extension method based on boundary local characteristic scale is proposed. The approach improves better the impact of boundary effect on EMD.
     1 对HHT采样频率、终止准则、曲线拟合、边界处理以及模态混叠等问题进行了分析,并基于HHT的时间特征尺度概念,提出了一种新的边界处理方法:边界局部特征尺度延拓法,较好地改善了边界效应对EMD分解的影响。
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  “时间特征尺度”译为未确定词的双语例句
     On the differences in determining the time and vertical velocity characteristic scales and analyzing high order derivatives suggested by certain authors, discussions are made and preliminary conclusions are drawn, namely,τ~L/U, W≤ MUH/L and  nf/x n~F/L n,n>1 for a single scale motion.
     对在一些作者中 ,在决定时间特征尺度、垂直速度特征尺度和分析高阶微商量级上的分歧进行了讨论 ,得出了初步结论。 即对于单一尺度的运动 ,τ~ L/U、W≤ MUH/L 和 nf/ xn~ F/Ln。
短句来源
     Empirical mode decomposition (EMD) method is a new tool for the nonlinear and non-stationary signals analysis, and can adaptively decompose signals intoseveral intrinsic mode functions (IMFs) according to its characteristic time scale.
     模式分解技术是一种新的非线性非平稳信号的分析方法,可以根据信号自身的时间特征尺度进行自适应分解,得到若干个基本模式分量。
短句来源
     It is based on the local feature of the signals, andcan adaptively decompose signals into several intrinsic mode functions (IMFs)according to its characteristic time scale.
     该方法基于信号的局部特征,可以根据信号自身的时间特征尺度进行自适应分解,得到若干个基本模式分量。
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  相似匹配句对
     Multiple-Time-Scale Features of Summer and Autumn Rainfall in Chongqing Region
     区域降水的多时间尺度特征
短句来源
     On the Time Scales
     关于时间尺度
短句来源
     (5) time is feature.
     (五)时间特征
短句来源
     (5) time is feature.
     5.时间特征
短句来源
     Multi-scale Characteristics of the Urban Heat Island Intensity in Shanghai
     上海市城市热岛效应的时间尺度特征
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On the differences in determining the time and vertical velocity characteristic scales and analyzing high order derivatives suggested by certain authors, discussions are made and preliminary conclusions are drawn, namely,τ~L/U, W≤ MUH/L and  nf/x n~F/L n,n>1 for a single scale motion. Based upon them the orders of magnitude of several characteristic scales currently used for atmospheric motion are presented, and then compared with the statistical observations in mesoscale gravity wave and severe convection...

On the differences in determining the time and vertical velocity characteristic scales and analyzing high order derivatives suggested by certain authors, discussions are made and preliminary conclusions are drawn, namely,τ~L/U, W≤ MUH/L and  nf/x n~F/L n,n>1 for a single scale motion. Based upon them the orders of magnitude of several characteristic scales currently used for atmospheric motion are presented, and then compared with the statistical observations in mesoscale gravity wave and severe convection cases.

对在一些作者中 ,在决定时间特征尺度、垂直速度特征尺度和分析高阶微商量级上的分歧进行了讨论 ,得出了初步结论。即对于单一尺度的运动 ,τ~ L/U、W≤ MUH/L 和 nf/ xn~ F/Ln。根据讨论结果 ,给出了大气几种常用尺度运动的特征尺度的量级。还与中尺度重力波和强对流情况的分析观测结果进行了比较。

Due to the fluctuation and complexity of electromechanical equipment operation condition affected by various factors, it is difficult to use a single forecasting method to accurately describe the moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD), support vector machines (SVMs) and adaptive linear neural network (ALNN), is proposed, where these intrinsic mode components (IMCs) are adaptively extracted via EMD from a nonstationary time series (according)...

Due to the fluctuation and complexity of electromechanical equipment operation condition affected by various factors, it is difficult to use a single forecasting method to accurately describe the moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD), support vector machines (SVMs) and adaptive linear neural network (ALNN), is proposed, where these intrinsic mode components (IMCs) are adaptively extracted via EMD from a nonstationary time series (according) to the intrinsic characteristic time scales. Tendencies of these IMCs are forecasted with SVMs (respectively,) in which the kernel functions are appropriately chosen with these different fluctuations of IMCs. These forecasting results of IMCs are combined with ALNN to output the forecasting result of the original time series. The proposed model is applied to the tendency forecasting of a benchmark example and a vibration signal from machine sets, and the simulated results show that the forecasting performance (of the) hybrid model outperforms SVMs with the single-step ahead forecasting or the multi-step ahead (forecasting.)

针对机电设备运行状态受多因素影响且变化趋势复杂、难以用单一预测方法进行有效预测的问题,提出了一种新的基于经验模式分解、支持向量机和自适应线性神经网络的混合智能预测模型.首先,利用经验模式分解方法将非平稳时间序列按其内在的时间特征尺度自适应地分解为多个本征模式分量,然后根据这些分量各自趋势变化的剧烈程度选择合适的核函数,用支持向量机对其进行预测,最后通过自适应线性神经网络对这些预测分量进行自适应加权组合,得到原始序列的预测值.研究结果表明,对于标准算例和某机组振动趋势的预测,不论是单步预测还是多步预测,该模型的预测性能均好于单一的支持向量机预测方法.

 
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