本文提出了一种分级实现的模糊聚类算法。
A hierarchical fuzzy clustering algorithm is presented in this paper.
而模糊聚类算法则准确地完成对样本的分类任务。
Fuzzy clustering algorithm was used to classify the faults samples correctly.
本文给出了模糊聚类算法在图像分割中的应用结果。
In this paper, the application of suppressed fuzzy clustering algorithm in image segmentation is introduced.
针对彩色地形图的自动分色问题，提出了一种新的模糊聚类算法。
Aimed at the automatic color segmentation of topographic maps, a new effective fuzzy clustering algorithm is presented.
模糊聚类算法(FCM)应用于数字图像的边缘检测已取得了较好的效果。
Fuzzy c-clustering algorithm (FCM) is a useful tool in edge detection of digital image.
目的介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。
Objective to introduce a dynamic fuzzy clustering algorithm and use it to do the study of segmentation of the brain in MRI.
针对偏置环境下图像分割问题，提出了一种基于偏置场估计的模糊聚类算法。
A novel FCM segmentation algorithm is proposed based on bias field estimation with respect to the segmentation issue of defocused images with illumination patterns under bias field.
在此基础上，对于标准模糊聚类算法进行了改进并应用于舌象的舌质舌苔分离。
So standard FCM algorithm was modified and applied to separate the tongue body and tongue coating.
在传统模糊c -均值聚类算法的基础上，提出了一种新型区间值数据模糊聚类算法。
Based on the traditional fuzzy C-means clustering algorithm, a new fuzzy C-means clustering algorithm for interval data clustering is proposed.
在模糊聚类算法的基础上，提出了一个衡量聚类有效性的函数，以确定模糊规则的数目。
A function for measuring clustering validity based on the fuzzy clustering algorithm is defined with which the number of fuzzy rules can be determined.
然后介绍了如何使用模糊聚类算法和等价的前馈神经网络从样本数据中辨识离散的TS模型。
Then we introduce how to identify the TS model from sample data using fuzzy clustering algorithm and equivalent feedforward neural network.
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.
同时改进了现有的直觉模糊聚类算法中的概率型约束条件，使其对噪声和野值点具有较好的鲁棒性。
Besides, it was robust to the noises because it improved the constraint conditions used in the existing intuitionistic fuzzy clustering algorithm.
为解决此问题，提出一种基于捕食-被捕食的粒子群优化模糊聚类算法且聚类中心采用密度函数初始化。
To solve the problem, a fuzzy clustering based on predator prey PSO algorithm is presented, which is using density function to initialize cluster centre.
本文利用改进的模糊聚类算法，依据邻域信息实现了对丢失图像信息的恢复，并完成了对该图像的检测。
In this paper, an improved FCM algorithm (CFCM) is given, through which the lost information of in-complete-data images can be restored, while edge-detection of images is accomplished.
本文在研究模糊聚类算法和火灾图像特点的基础上，提出了一种基于改进FCM算法的彩色火灾图像分割方法。
A modified FCM algorithm for fire image segmentation is proposed in this paper based on the study of fuzzy clustering algorithm and characteristics of fire images.
该方法借鉴了神经网络理论、模糊聚类算法和自适应模式识别法的优点，自动完成样本的分类与样件设计工作。
Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, the method can be used to classify and design the sample workpiece automatically.
针对间歇生产过程的配方缺少定量分析方法，难以用于过程建模和控制策略实施的问题，提出了一种基于类核函数的配方模糊聚类算法。
This paper presents a new fuzzy cluster analysis method for batch process recipe that has less quantitative analysis ways before and is difficult to be used for modeling or controlling system.
这些算法在聚类过程中存在模糊性。
Basically there is fuzziness in the procedure of clustering.
针对新的参考向量开发了模糊竞争学习模式，并用该算法成功地解决了文献聚类的难题。
This paper also develops a fuzzy competitive learning scheme for these new reference vector parameters, and applies the algorithm to the difficult task of clustering documents.
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.
通过对模糊c均值算法聚类特性的分析，引入了约束函数及模式相似度的概念，提出了改进的FCM算法。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM is presented.
结果模糊K- 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、 白质和脑脊液。
Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
针对模糊C均值聚类算法对初始值敏感、易陷入局部最优的缺陷，提出一种新的优化方法。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
论文采用了一种基于改进的模糊C均值算法来聚类图像。
This paper proposes a modified fuzzy C-means (MFCM) clustering algorithm to cluster all images before retrieval.
本文将经典的模糊c -均值聚类算法和模糊测度和模糊积分结合起来，并将这两种算法应用于医学病理图象的分割。
In this article we combine the fuzzy C-means algorithm with fuzzy measures and fuzzy integrals and apply the two algorithms to the medicinal pathological image segmentation.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
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