The iterative algorithms in C language were given, which used direct data replacement to reduce the dimension of matrix and saved the storage space.
在迭代算法中，采用直接数据置换降低矩阵维数，节省了存储空间，提高了运算速度。
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.
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 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。
Simplified algorithms are described based on C-W equation, which are valid in short time.
并根据C-W方程推导了只在短时间内有效的简化碰撞概率算法。
Numerical results are in good agreement with experiments. Advantages of the numerical algorithms are saving c…
算法的优点在于计算域小、计算时间缩短、共形性好及子域建模程序具有可移植性等。
Lots of robust fuzzy C-means algorithms have been proposed in the literature to solve this problem.
针对这个问题，很多稳健模糊C -均值聚类算法被提出。
Its algorithms and experimental results in C language are given.
给出排序算法及用C语言编程进行实验的结果。
All these algorithms are implemented in C and verified by examples.
调度算法均用c语言实现，并经实例验证。
All these algorithms are implemented in C and verified by examples.
调度算法均用c语言实现，并经实例验证。
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