parameter clustering
英
美
[计] 参数群
双语例句
- Parameter Weight of Weighted Clustering Algorithm of Ad hoc Networks
adhoc网络权值分簇算法的参数权重 - Load Dynamic Characteristics Synthesis Based on Parameter Clustering of Induction Motor
基于感应电机参数聚类的负荷动特性综合方法 - Secondly, the paper builds parameter space histograms through Hough transform and detects tracks by clustering parameter space histograms.
采用Hough变换建立参数空间直方图，并对参数空间直方图聚类实现航迹探测； - The Study of Parameter Choice in Fuzzy Clustering Segmentation for MRI Brain Images
磁共振脑图像模糊聚类分割中的参数选择问题研究 - Real-time Traffic Parameter Estimating Based on Mobile Phone Locating and Clustering
基于手机定位及聚类分析的实时交通参数估计 - This paper discusses speaker adaptation technique from speaker normalization, model parameter adaptation and speaker clustering.
本文通过对模型参数自适应，说话人归一化和说话人聚类三方面对说话人自适应技术进行了讨论。 - Besides, it can automatically control the resolution parameter during the clustering proceeds and overcome the sensitivity to noise and outliers.
该算法还能在聚类过程中自动地确定分辨率参数，克服了对于噪声和外围点的敏感性。 - In fact, it can also be used to solve other multi-parameter threshold optimizing problems such as rule boundary optimizing, parameter threshold optimizing in classification and clustering problems, etc.
本方法不仅适用于动词-动词搭配参数阈值的选取，也适用于其它多参数阈值选取问题，如规则边界优化，分类与聚类参数阈值优化等。 - An Optimal Choice Method of Parameter m in FCM Clustering Algorithm
FCM聚类算法中模糊加权指数m的优选方法 - The simulation results for the network parameter such as the clustering coefficients, average path length and eigenvalue λ 2 and λ n of Laplacian matrix for these two models are presented. The consensus problems that based on these two models are studied.
本文给出了这两个模型的网络参数聚集系数、平均路径长度、Laplacian矩阵特征值λ2和λn的特性的仿真结果，对基于这两个模型的一致性问题进行了研究。 - Attributive fuzzy clustering and association rule calculation. An uncertain description of all the boiler's operational parameter is obtained by the attributive fuzzy clustering method and representative parameters are selected;
该方法融合属性模糊聚类和关联规则算法两种数据挖掘技术，利用属性模糊聚类方法建立锅炉各运行参数对类别的不确定性描述，选择出代表性分析参数； - The Method of Surface EMG Pattern Recognition Based on AR Parameter Model and Clustering Analysis
基于AR参数模型与聚类分析的肌电信号模式识别方法 - Experiments show that the automatic parameter setting in document clustering generates more satisfied clustering result and improves the clustering efficiency.
实验表明，参数自动设置技术使文本聚类过程更加自动化，同时提高了文本聚类的质量和效率，产生了更好的聚类结果。 - Because of the complexity of decomposition, the problem of sEMG decomposition was translated into three-base-function parameter optimizing and parameter clustering of the same SFAP.
由于sEMG分解的复杂性，本研究将sEMG分解问题转化为SFAP三基函数参数的优化问题和同一SFAP参数的聚类问题。 - Set Parameter K in K-means Clustering Using Genetic Algorithm
使用遗传算法实现K-means聚类算法的K值选择 - Furthermore, we give an evaluation criterion of normal reconstruction to adaptively adjust the parameter of hierarchical clustering.
进而算法给出一个法向重建效果评估准则，以自适应地优化算法参数[0]。 - The new algorithm extends the original one by introducing a suppression threshold parameter, and improves the result of clustering while keeping the convergence speed less changed.
通过对抑制式模糊C-均值聚类算法的良性扩展，在保持收敛速度变化不大的前提下改善了聚类的效果。 - In order to offer an method for parameter selection of partition num-ber, this paper proposes to learn the parameter k in unsupervised clustering K-means using genetic algorithms, such that the number of clustering partitions can be achieved before clustering.
文章使用遗传算法对无导师聚类K-均值（K-means）算法中中心个数K值进行学习，实现了使用遗传算法进行聚类中心个数的确定，旨在提供一种选择中心参数个数的方法。 - Study and Implementation of Automatic Parameter Setting for Document Clustering
文本聚类中参数自动设置技术的研究与实现 - Based on that the grid clustering algorithm is sensitive to parameters, we advance a grid-based clustering algorithm with the parameter automatization ( PAG) that can solve the problem that the grid clustering algorithm is sensitive to parameters;
针对网格聚类算法对参数敏感的问题，提出了一种基于网格的参数自动化聚类算法，该算法使用参数自动化技术解决了算法对参数敏感的问题； - Uses the Genetic Algorithms for optimizing the membership function parameter, utilizes self-organizing close neighbor clustering law to process a pair of in-out data and uses the method which generates the fuzzy rules and linguistic rules to obtain fuzzy rules.
采用遗传算法寻优隶属函数参数，利用自组织近邻聚类法处理输入输出数据对，结合数据产生模糊规则和语言规则的方法得到模糊规则。 - Using MATLAB simulation, the corresponding network maps, characteristics distribution diagrams and the three network characteristics parameter values-characteristic path length, clustering coefficient and average degree-are got.
用MATLAB进行仿真，得到相应的网络图和特征分布图及特征路径长度、聚类系数和平均度三个网络特征参数值。 - Through algorithm performance analysis with the different parameter and compares experiment with the classical clustering algorithms, the results show that the above-mentioned clustering algorithm kernel-based learning method has good stability, and has the significant advantage of an ideal clustering effect.
通过算法在不同参数下的性能分析，以及与经典聚类算法的比较实验，结果表明，上述基于核学习方法的聚类算法具有算法稳定性好，且聚类效果理想的显著优势。 - Kernel parameter of the SVC algorithm plays an important role in clustering formation, which affects the boundary and shape of cluster.
SVC算法中的核函数参数对聚类的形成起着决定性的作用，并影响着聚类的边界和形状。 - The influence of the parameter selection of kernel fuzzy C-means clustering ( KFCM) on medical ultrasound image segmentation result is studied.
研究了核模糊c均值聚类（KFCM）参数的选取对医学超声图像分割结果的影响。 - According to the characteristics of human behavior, it adopts non-cross-type HMM; while during the process of parameter determination of the RBF neural network, it adopts the nearest neighbor clustering algorithm.
根据人体行为的特点，设计了一种无跨越左右型的隐马尔科夫模型；而在径向基神经网络的参数确定过程中，采用了动态最近邻聚类算法来确定网络的中心。 - Fuzzy C-Means algorithm is a based on objective function is the vague and class methods and the result is dependent on the clustering of a parameter previous experience knowledge ( clustering center and clustering number).
FCM（fuzzyC-Means，FCM）算法是一种基于目标函数优化的模糊聚类方法，其收敛结果依赖于聚类原型参数的先验知识（即聚类中心和聚类数）。 - For all syndromes cluster analysis, clustering method selection system clustering R cluster method, parameter selection farthest neighbors, clustering range of options pearson correlation.
对所有中医证候进行聚类分析，聚类方法选择系统聚类中的R型聚类法，参数选择最远邻元素，聚类区间选择pearson相关性。 - 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。 - Genetic variation parameter statistics, population differentiation analysis and clustering analysis were carried out to determine the genetic diversity and evolutionary relationships among Chinese horse populations.
通过统计各种遗传多样性参数、分析群体遗传分化程度和系统聚类，研究了中国地方马群体遗传多样性和群体间遗传关系。