所有报告的样本误差比率包括了加权和抽样设计设计效应计算在内。
All reported margins of sampling error include the computed design effects for weighting and sample design.
我们认为这是因为小样本误差和数据所在时间跨度较短所造成的。
We guess this is due to the small sample bias and the short time span of our data.
除了样本误差外，全国性的公众意见的调查操作上的困难也会导致误差。
In addition to sampling error, the practical difficulties of conducting any survey of public opinion may introduce other sources of error into the poll.
原始团体越小，样本得出结论的误差越大。
For smaller subgroups, the margin of sampling error is larger.
分析唾液样本可以推断出一个人的年龄，且误差在五岁以内。
Analyzing a saliva sample can determine an individual's age to within five years.
该模型有自组织和自学习的功能，可以根据每次学习误差的不同，不断调整学习速率，加速收敛过程，充分排除数据样本的随机性影响。
The network model can organize and study itself, according to different study error, continuously adjust the study rate, and accelerate refrain process, expel influence of the data sample.
利用误差样本平均归一化自相关函数，可以对所抽取的数据独立性进行验证，同时为改进数据处理方法提供依据。
To validate the independency of data, we can using the average autocorrelation function of error swatch, it can also offer thereunder to ameliorate the method of disposing data.
该方法在样本点相同的情况下减小了近似模型的推广误差，提高了近似精度，增加了适应性。
By using this method the extended error of the approximation model is diminished, the approximation precision is improved, and the flexibility is enhanced based on having the same sample points.
影响抽样误差大小有很多因素，如标志变异程度，样本单位数量的多少，抽样方式方法的选择等。
There are many factors that influence the sampling error, such as, extent of mark variation, amount of sample unit and the choice of sampling method, etc.
本文提出了一种有限样本集上基于次特征值误差补偿和优势主向量上非对称分布的马氏距离改进算法。
A modification on Mahalanobis distance on samples of limited size by compensation for errors of non-dominant eigenvalues and asymmetrical distribution on dominant principle components is proposed.
条件的影响是有匹配样本的连续调查特有的计量误差来源。
Condition effect is an important source of non-sampling error in continuous surveys with matched samples.
改进后的聚类结果既消除了采样误差，又保持了云类样本的基本特征属性。
Therefore, the improved FCM clustering results can reduce the sampling errors and retain the main attributes of cloud classification samples.
分析了多项式曲线与样本点的误差。
The errors between the polynomial curve and samples order were analyzed.
本文主要论述块状矿石显微镜下矿物含量点测法的估计定律，以及此定律的简要证明、测量统计中最低限度样本容量的确定及误差范围。
In this paper, estimating law for mineral content, simple proof of the law and determining lowest sample size and error range in measure and statistics are discussed.
并介绍了在线误差补偿硬件系统的实现方法，以及通过样本的合理选择和系统的学习来提高补偿系统的补偿能力。
The realization method of lined error compensation hardware system and the methods of advance its compensation ability by the reasonable selection swatch and by the system study were introduced.
实际计算结果表明，当样本组数小于5时，控制方法的计算误差仅为正态逼近法的20%。
The results of calculation show that the error of proposed approach is only 20% of that by the traditional approach, while the sample groups are less than 5.
样本输出结果较理想值误差较小，分类器的识别结果完全符合实际情况。
The errors between output and ideal results of training samples were smaller.
此分类算法首先计算未知类别样本的重构系数，定义一种误差作为判别标准，根据此误差的大小判断样本的类别归属。
This algorithm firstly computes the reconstruction weights of unknown samples. Then an error, on which the class of samples can be decided based, is defined as a criterion.
神经网络的理论基础是最小化经验误差，这种基于传统的渐进理论的学习方法，在训练样本点无穷多时是适用的。
Because neural network is based upon empirical risk minimization and asymptotic theories, it is suitable to deal with situations where the amount of samples is tremendous and even infinite.
用训练样本集对网络训练后，检验样本的预测结果与实际值最大误差为0.97%。
The model was trained with training sample aggregation. The maximum error between the forecasted and real value was 0.97%.
引入的算法增大了大值输出样本和期望输出的误差，使得网络向着提高洪峰拟合精度的方向修改权重。
The algorithm expands the error between the big value stylebook and anticipant output, making the network modify the weight toward the direction of higher simulating precision to flood peak.
基于不同的样本数，用响应面方法寻求的最佳设计点在大致相同的区域，而且与CFD分析结果相比误差很小。
The optimum design points with RSM based on different samples are almost in the same area and there is little error compared with the CFD result.
应用神经网络的误差反向传播算法(BP)和大量的实测数据样本训练出了能在线诊断四种加工状态的BP模型并成功地诊断了实际加工状态。
The BP algorithm of Artificial Neural Networks and lots of experimental samples were used in training the BP model which succeeded in diagnosing four kinds of operational status.
最后，利用样本训练BP网络，待误差满足要求后，即可运用训练成功的BP神经网络，进行矿井安全状况综合评价。
At last, we can use samples to train BP network and assess the coal mines' safety status by using the successful BP network after error meets a demand.
功率谱减法是一种传统的降噪方法。 但是功率谱减法采用固定的无音片段作为噪声样本容易引起误差。
Power spectral subtraction is a traditional method for decreasing noise which easily leads to error by regard fixed silence fragment as noise sample.
为了减小定位误差和提高算法的适应性，利用三维空间抽样和范围约束的方法，并结合对成功样本点的加权筛选，获得节点的三维估计坐标以实现抽样定位。
Combined with weighted filtration to successful sample points, it uses the method of sampling in three-dimensional space and range constraint to acquire three-dimensional coordinates of nodes.
通过多个实验表明，试样的均衡性、读数误差控制范围的正确确定以及样本菌量的正确估计，都是影响实验室菌落计数精密度控制的因素。
The results show that homogeneity of samples, correct determine of control range of reading errors and correct estimation of bacterium account are elements that affect accuracy of aerobic plate count.
通过多个实验表明，试样的均衡性、读数误差控制范围的正确确定以及样本菌量的正确估计，都是影响实验室菌落计数精密度控制的因素。
The results show that homogeneity of samples, correct determine of control range of reading errors and correct estimation of bacterium account are elements that affect accuracy of aerobic plate count.
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