The author proposes a new self tuning fuzzy modeling by means of fuzzy competitive learning.
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 Antibody Network (ABNET), which is a new ANN based on immune principle, has been proved to have good ability of unsupervised and competitive learning in experiments.
抗体网络作为一种新型的基于免疫原理的神经网络模型，已有的实验验证了其具有良好的无监督竞争学习能力。
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.
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。
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.
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。
Neural network competitive learning algorithms are widely used for vector quantization. In this paper, some typical competitive learning algorithms have been specially investigated and analyzed .
对典型的竞争学习算法进行了研究和分析，提出了一种基于神经元获胜概率的概率敏感竞争学习算法（PSCL）。
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.
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。
The Antibody Network (ABNET), which is a new Artificial Neural Networks (ANN) based on immune principle, has been proved to have good ability of unsupervised and competitive learning in experiments.
抗体网络作为一种新型的基于免疫原理的神经网络模型，已有实验验证了其具有良好的无监督竞争学习能力。
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。
This paper proposes to overcome those problems by incorporating the improved RPCL (rival penalized competitive learning)algorithm and the EM(expectation maximization)algorithm into the EBF structure.
本文提出用结合改进的RPCL算法和EM算法的EBF网络结构来解决上述问题。
Competitive learning is an unsupervised learning.
竞争学习是一种无监督学习。
In this paper, the grey theory is applied to a two-layer Modify Competitive Learning Network (MCLN) in order to generate optimal solution for VQ.
本篇论文中，灰色理论被应用到一个两层的修正竞争式学习网路上。
In this paper, the grey theory is applied to a two-layer Modify Competitive Learning Network (MCLN) in order to generate optimal solution for VQ.
本篇论文中，灰色理论被应用到一个两层的修正竞争式学习网路上。
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