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。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
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。
In order to getting the effective training data of chemical engineering modeling, two algorithms that fuzzy C-means and fast global fuzzy C-means clustering were used.
分别采用模糊c -均值聚类方法和快速全局C -均值聚类两种算法实现化工建模所需训练数据的有效提取。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
Several new algorithms of fuzzy C-mean clustering with the combination of vector quantization are proposed for speaker identification.
该文提出了一种将模糊C -均值聚类法的各种改进算法与矢量量化法相结合的说话人辨认的新方法。
A method based on fuzzy C-mean clustering and image matching algorithms are proposed to detect atomization Angle and uniformity, applied to performance test-bed of engine nozzle.
提出了基于模糊C均值聚类和图像匹配，检测喷雾锥角和喷雾不均匀度的方法，并应用于发动机喷嘴性能检测。
At last we deeply studies the methods of optimizing the structure of fuzzy clustering, and proposes two algorithms.
三是深入研究模糊聚类神经网络的结构优化方法，提出两种模糊聚类神经网络优化方案。
At last we deeply studies the methods of optimizing the structure of fuzzy clustering, and proposes two algorithms.
三是深入研究模糊聚类神经网络的结构优化方法，提出两种模糊聚类神经网络优化方案。
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