提出了一种新的基于模糊竞争学习的自调整的模糊建模方法。
The author proposes a new self tuning fuzzy modeling by means of fuzzy competitive learning.
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.
先通过基于模糊竞争学习确定一种在线模糊辨识算法，并给出递推模糊竞争学习算法收敛性证明。
First of all, an on-line fuzzy identifying algorithm is confirmed by means of fuzzy competitive learning, and the convergence about a recursive algorithm of fuzzy competitive learning is proved.
首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数。
First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm.
基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。
The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained.
基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。
The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained.
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