In this thesis, the design and realization of a special cluster are presented for calculating branch and bound algorithm that can decrease the calculating time and enhance the algorithmic efficiency.
本文设计实现的一种专用于计算分支定界算法的机群计算平台，能减少分支定界算法的计算时间复杂度，提高分支定界算法的效率。
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。
This paper presents a parallel algorithm for Lagrange's polynomial interpolation which is based on cluster parallel environment.
提出在机群系统并行环境下的构造拉格朗日插值多项式的一种并行算法。
A cluster algorithm of automatic key frame extraction based on adaptive threshold is employed and mended for extracting shot's key frames.
在镜头关键帧提取时实现并改进了基于自适应阈值的自动提取关键帧的聚类算法。
Next the general method of applying fuzzy ARTMAP model to feature level fusion is also expounded and we put forward a learning algorithm with adaptive vigilance parameters for each cluster.
继而研究了模糊artmap网络用于特征层融合识别的方法，并提出了一种网络警戒参数自适应调整新算法。
In this paper, we propose a new clustering algorithm based on cluster validity indices, which obviates the needs for cluster parameters.
本文提出了一种新的基于有效性指数的聚类算法，无需提供聚类的参数。
The new algorithm chooses node which holds a higher connectivity to be a cluster head, so that the cluster head elected would be more appropriate for the topology.
改进后的算法选择连通度高的节点优先成为簇首，这样选择出的簇首更加利于簇的管理与维护。
The algorithm presents a clear and precise membership function for every cluster after data preprocessing by traditional grid partition.
在定义隶属度函数前先做网格划分，形成数据簇的基本形状，并提供真实的参数信息参与此后的隶属度函数定义。
As for it, by improving learning algorithm of traditional RBF neural network, a new dynamic cluster-based self-generated method for hidden layer nodes is proposed.
对此，本文改进了R BF神经网络的学习算法，提出了一种基于聚类的动态自生成隐含层节点的思想。
As for it, by improving learning algorithm of traditional RBF neural network, a new dynamic cluster-based self-generated method for hidden layer nodes is proposed.
对此，本文改进了R BF神经网络的学习算法，提出了一种基于聚类的动态自生成隐含层节点的思想。
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