This paper presents a new leaning method for radial basis function network, minimum mean entropy difference criterion algorithm is used to get pattern cluster of training sets.
本文对径向基函数网络提出了一种新的学习算法，利用最小均熵差准则对训练样本进行模式聚类。
Based on maximum between-cluster variance method and uniformity measure, this paper USES maximum entropy principle to select the gray-level threshold value for image segmentation.
在最大类间方差法和一致性准则法的基础上，运用最大熵原理来选择灰度阈值对图像进行分割。
To overcome these shortcomings, an improved training method of RBF networks, the method of output-input cluster based on the minimum entropy theory isp.
目前已有的几种RBF网络训练方法对于含有随机噪声的复杂样本训练速度过慢且分类性能不稳定，依据相对熵最小原理，提出了一种改进的RBF网络训练方法———输出-输入聚类法。
Performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimum cluster number.
利用两个聚类效果评价指标模糊效果指数FPI和归一化分类墒NCE，确定了最适宜的分区数。
Performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimum cluster number.
利用两个聚类效果评价指标模糊效果指数FPI和归一化分类墒NCE，确定了最适宜的分区数。
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