method of clustering center
英 [ˈmeθəd ɒv ˈklʌstərɪŋ ˈsentə(r)]
美 [ˈmeθəd əv ˈklʌstərɪŋ ˈsentər]
网络 聚合中心法
双语例句
- The premise parameters 'identification consists of two steps: Start from an initial fuzzy partition of input space by a nearest-neighbor clustering method to get the number of rules and the initial clustering center;
首先利用最近邻聚类法初始划分输入空间，得到规则数及初始聚类中心，用模糊C均值算法（FCM）进一步优化聚类中心； - A method of moving targets tracking based on fuzzy entropy clustering and Kalman filter forecasting is advanced, and the forecasting value of Kalman filter as the center of next frame cluster is used. This method reduces the iterative numbers and increases the real-time tracking.
本研究提出了基于模糊熵聚类和Kalman滤波预测的区域跟踪方法，用Kalman滤波的预测值作为下一帧图像运动区域的聚类中心，从而减少了迭代次数，加强了跟踪的实时性。 - The algorithm can overcome the shortcomings of the seeking method of initial value of the clustering center of SCA algorithm, and gives a method of combining maximum frequency degree with maximizing minimum discrepancy, that is an optimum seeking method of initial value of clustering center.
本算法克服了SCA算法对聚类中心初始值选取的不足，给出了最大频度与类内最小距离最大相结合的方法&初始值优选法。 - We adapt new amendatory method to quicken constringency speed and enhance clustering effect, for the calculation of membership matrix and clustering center, and a new algorithm of fuzzy clustering is put forward.
对于算法中隶属度矩阵和聚类中心的计算，我们采用了新的修正方法以加快收敛速度和提高聚类效果，并由此提出了模糊聚类的新算法。 - On this basis, we analysised two old methods based on distance and density of basic idea, and found a new method based on high density of initial clustering center algorithm.
在此基础上，融合了基于距离和基于密度的聚类算法的基本思想，采用了基于高密度的初始聚类中心算法。 - This method uses the center of gravity of all the samples in a dynamic load characteristics classification as the clustering center of this classification, and by doing parameter identification of the clustering center equivalent sample, the synthetic load model of this classification is gained.
该方法首先通过求得某类负荷特性中所有样本的重心作为该类负荷特性的聚类中心，再通过对聚类中心等效样本进行参数辨识以得到该类负荷特性的综合模型参数。 - We first select initial cluster centers by method of Subtractive Clustering, then weight the objective function using density function to adjust cluster center.
先利用减法聚类的方法选定初始的聚类中心，再对目标函数利用密度函数加权来调整聚类中心。