sampling cluster meaning in Chinese
成团抽样
Examples
- Besides , considering the differences of the galactic gravitational potential models , there is different influence for the distribution morphologies of all of the orbital parameters of the selected sample clusters
此外，不同的银河系引力势模型对所选样本的轨道参数的分布形态也有不同程度的影响。 - The differences in orbital morphologies due to different potentials is slighting , however , given a certain potential , for clusters that have perigalactic distance smaller than 1 kpc , some orbits may exhibit a chaotic behavior . the correlation between the metallicity of samples and the orbital morphologies is unclearly ; ( 4 ) it is found that the semi - major axis , apogalactic distance and azimuth period of 29 sample clusters are changed with their metallicity similarly , but a obvious correlation is seen between orbital eccentricity and metallicity . there is a fraction of 24 % of the sample clusters with eccentricities lower than 0 . 4
不同的引力势模型对球状星团轨道的具体形态影响不大，在给定的引力势模型下，当某些星团的运动轨道穿越距银心1kpc附近的区域时会出现“混沌”现象： ( 4 ) 29个样本星团的轨道半长轴、远银心距和方位周期随金属度的变化规律基本相似，样本星团的金属度与其轨道形态之间的相关性并不明显，然而轨道偏心率与金属度有关，对于所选的晕族样本星团而言，大约有24的样本星团的轨道偏心率低于0 . 4 。 - The simulation tests result indicates that the speed and precision of sample training are increased because of sample clustering for fuzzy modular networks . and the problem of slow training speed and local minimum point are avoided when bp networks are applied in the fault diagnosis of complex boiler
本文所建的用于锅炉故障诊断的模糊模块化神经网络模型因进行了样本聚类，实验结果表明：其网络训练的速度和精度明显提高，同时有效地解决了bp网络应用于复杂的锅炉系统故障诊断时，存在训练收敛慢并容易陷入局部最小点的问题。 - In this paper , the near - infrared spectral measurement and principal component analysis ( pca ) of simulated spilled petroleum samples are made , the princpal component scores are taken as the cluster characteristic variables for making fuzzy clustering , the near infrared spectrometry and pca together with fuzzy clustering method are then used for rapid recognition of simulated spilled oils , and the sample clustering and recognition patterns are established
摘要对模拟溢油的石油类样品进行近红外光谱检测并进行了主成分分析，以主成分得分作为聚类的特征变量进行模糊聚类，采用近红外光谱测量结合主成分分析和模糊聚类法快速识别模拟溢油种类，并建立了样品聚类与识别模型。 - The calculating results show : ( 1 ) most of samples are located in 5 kpc - 10 kpc from galactic center . all of the sample clusters present a spherical symmetrical distribution around the galactic center , and their space velocities are presented a ellipsoidal distribution ; ( 2 ) according to the metallicity and basic characters , the sample clusters are separated into hb subgroup and mp subgroup . the number of samples are changed with metallicity [ fe / h ] , and there is a peak at [ fe / h ] = - 1 . 6 ; ( 3 ) the orbits of sample clusters show mostly limited , periodic characteristics , but the orbits are not closed completely , their maximal galactocentric distance is less than 40 kpc
计算结果表明： ( 1 )大部分样本星团都位于银心距5kpc 10kpc的范围内，相对于银心呈球对称分布，它们的速度也呈椭球分布； ( 2 ) 29个样本星团按其金属度大小和基本性质分类，可分属hb和mp两个球状星团次系，且样本星团数随金属度[ fe h ]而变化，在[ fe h ] = - 1 . 6处出现一个峰值； ( 3 )所有样本星团的轨道运动都呈周期性，大都在一个有界而不封闭的周期轨道上运动，其最大银心距都在40kpc以内。