针对异类传感器观测空间不一致的问题，提出了基于模糊聚类的异类多传感器数据关联算法。
For the inconsistency problem of heterogeneous sensors' measurement Spaces, a new data association (da) algorithm based on fuzzy clustering algorithm is presented.
为了克服传统FCM算法的局限性，本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法（2DFCM）。
In order to overcome the limitation of FCM, a novel Two-dimension Fuzzy Cluster Method (2DFCM) was proposed based on the spatial information.
该模型利用模糊聚类技术确定系统的模糊空间和模糊规则数，利用BP算法调整模糊神经网络的权系数。
The fuzzy space and the number of fuzzy rules of this model are defined by the fuzzy clustering method and weight coefficients of the model are adjusted by the BP algorithm.
本文从不同树种、不同时间、空间的分布出发，应用模糊聚类分区模型先对样本进行分类，以便确定有关参数。
The article applies fuzzy gathering distribution model and distribute species from different trees, time and space decides on the related parameter.
利用递推模糊聚类算法实时对系统的输入空间进行模糊划分，利用卡尔曼滤波算法确定参数。
The input space of fuzzy system is partitioned by means of real time recursive fuzzy clustering, and the parameters of fuzzy model are confirmed by Kalman filtering.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
通常的做法是在原来模糊c -均值聚类的目标函数中加入空间信息惩罚项。
A general solution is to add the spatial information to the object function of fuzzy C-means.
从定义各类型车辆所占用的动态空间瞬时车道占有率入手，提出了以车辆瞬时占用车道长度与速度的比值为参数，基于模糊动态聚类对车辆进行分类的方法。
The paper analyzes the characteristics of fuzziness of dynamic space occupancy of different vehicles on urban arterials, and classifies the different vehicles by means of fuzzy clustering.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
由于原始的模糊c -均值聚类算法没有考虑图像的空间信息，算法对图像中的噪音点十分敏感。
Without considering the spatial information of images, the original fuzzy C-means algorithm is very sensitive to image noise.
为了利用信息系统对象在数据空间中分布，通过对对象的模糊聚类，计算每一类在坐标轴上的统计值。
In order to use the distribution of objects in information system, statistical values are computed on an axis by fuzzy clustering.
为了利用信息系统对象在数据空间中分布，通过对对象的模糊聚类，计算每一类在坐标轴上的统计值。
In order to use the distribution of objects in information system, statistical values are computed on an axis by fuzzy clustering.
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