Finally, a numerical example shows the applicability of the FCM clustering algorithm.
最后通过一个算例说明了该聚类算法的具体应用。
A speaker recognition method based on improved FCM clustering with vector quantization is introduced in this paper.
提出了一种将改进的FCM聚类算法与矢量量化相结合的说话人识别的方法。
Therefore, the improved FCM clustering results can reduce the sampling errors and retain the main attributes of cloud classification samples.
改进后的聚类结果既消除了采样误差，又保持了云类样本的基本特征属性。
In the image analysis, FCM clustering algorithm can be effectively used for image processing, especially for hand-written numeral recognition.
在图像分析处理中，FCM聚类算法可以有效地用于图像处理，特别是手写数字识别中。
After analyzing the problems with present FCM clustering algorithm, an improved version of FCM clustering algorithm is proposed in this paper.
分析了现有FCM聚类算法存在的问题，提出了一种改进的FCM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.
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。
Based on choosing reasonable cooling schedule, the objection function for SA is set up according to FCM clustering, and the image segmentation algorithm based on SA and FCM clustering is implemented.
在合理选择冷却进度表的基础上，依据FCM聚类算法建立目标函数，实现了基于SA和FCM聚类的图像分割算法。
An efficient segmentation method based upon fuzzy c-means (FCM) clustering principles is proposed. The approach utilizes a pyramid data structure for the hierarchical ana - lysis of color images.
这里提出了一种高效的基于模糊c均值(FCM)聚类的彩色图像分割方法，它利用塔形数据结构对彩色图像进行多层分割。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM 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。
After clustering analysis by the improved FCM, the obtained cluster centers as input samples is used and then the principal component images can be obtained based on KPCA.
首先利用改进的FCM进行聚类分析，然后将获得的聚类中心作为输入样本，进行KPCA，从而得到主成分图像。
Since an FCM based self adaptive fuzzy clustering technique is employed to determine the proper structure of the FNN and set the initial weights in advance, the network can be trained rapidly.
由于预先运用基于FCM的自适应模糊聚类方法确定模糊神经元网络合理的结构，并设置网络的初始权值，从而可提高网络的训练速度。
The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
模糊c均值算法(FCM)是经常使用的聚类算法之一。
An improved color segmentation algorithm is presented based on weighting fuzzy c-means (FCM) clustering algorithm.
在加权模糊c -均值(FCM)聚类算法的基础上，对分色算法进行了改进。
The rambutan flesh was segmented using the FCM (fuzzy C-mean) clustering method after removing the background of the image.
首先用阈值分割法去除红毛丹背景，然后用模糊C均值聚类方法来分割果肉区域。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
The standard FCM algorithm is not only extremely time-consuming for clustering large data set, but also more sensitive to noise.
标准的FCM算法对大数据样本集进行聚类时极为耗时，而且对噪声比较敏感。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
This paper discusses the fuzzy C-means algorithm (FCM), one of the fuzzy clustering methods and clustering validity measurements.
本文讨论了模糊聚类中的模糊C均值算法和聚类有效性测度。
Fuzzy c-clustering algorithm (FCM) is a useful tool in edge detection of digital image.
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。
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.
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。
Then, expounds the theory of fuzzy set and fuzzy clustering, goes into details for the classical FCM algorithm, with a analysis for its performance and shortcomings.
接着阐述了模糊理论与模糊聚类的相关内容，详细介绍了经典的FCM算法，分析了它的性能及缺点。
According to the test, comparing with single FCM and APSO algorithm, this algorithm is more accurate in clustering and higher efficiency as well.
实验表明：新算法与单一的FCM和APSO算法相比聚类更准确，效率更高。
This method enormously accelerates the FCM algorithm while maintaining the clustering …
该方法使FCM算法运算速度大大提高，且不影响算法的聚类效果。
The proposed fuzzy clustering algorithm incorporates the discriminating vector into its update equations such that the obtained update equations do not take commonly-used FCM-like forms.
该算法将鉴别矢量引入迭代更新方程，因此其异于常见的FCM聚类方程形式。
The proposed fuzzy clustering algorithm incorporates the discriminating vector into its update equations such that the obtained update equations do not take commonly-used FCM-like forms.
该算法将鉴别矢量引入迭代更新方程，因此其异于常见的FCM聚类方程形式。
应用推荐