结果表明，SVM分类器的识别性能优于最小欧氏距离分类器，且KDDA特征的识别性能最优。
The experimental results show that the KDDA features achieve the best recognition performance, and the SVM classifier outperforms the minimum Euclidean distance classifier.
连续相位调制的最佳相于解调的误码性能取决于其最小平方欧氏距离。
The error performance of continuous phase modulation (CPM)with optimal coherent detection depends on its minimum squared Euclidean distance(MSED).
连续相位调制(CPM)的最佳相干解调的译码性能取决于其最小平方欧氏距离。
The error performance of continuous phase modulation (CPM) with optimal coherent detection depends on the minimum squared Euclidean distance (MSED).
通过以全体样本对全体类别加权广义欧氏权距离平方和最小为目标函数，建立了模糊聚类、识别与优选决策统一的理论与循环迭代模型。
With the minimum square sum of weighted Euclidean distances as the objective function, the unified theory and cyclical iteration model of fuzzy cluster, recognition and optimum decision are founded.
对未知目标，以其子像对库矢量的欧氏距离最小为分类准则，进行了识别模拟实验。
Using the subimage of an unknown target as feature vector and minimum distance rule for target recognition, experiments on simulated data are done.
本文在不减少其最小平方欧氏距离的前提下，提出了多进制CPFSK的减少状态格状图，从而减少了维特比的译码复杂度。
In this paper, a reduced-state trellis for M-ary CPFSK is presented under the condition that the MSED is not reduced therefore the complexity of Viterbi decoding is reduced.
采用平均最小自由欧氏距离估计误比特率，得到误比特率的封闭式表示，通过计算机模拟，在高信噪比情况下，这种估计方法与实际情况非常接近。
The bit error probability is estimated by the minimum average Euclidean distance. Through simulation, it is shown that the estimation is usually very accurate at high signal-to-noise ratio.
采用平均最小自由欧氏距离估计误比特率，得到误比特率的封闭式表示，通过计算机模拟，在高信噪比情况下，这种估计方法与实际情况非常接近。
The bit error probability is estimated by the minimum average Euclidean distance. Through simulation, it is shown that the estimation is usually very accurate at high signal-to-noise ratio.
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