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聚类网络
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  clustering network
     During the course of image segmentation, a clustering algorithm based on GA-FKCN(Genetic Algorithm-Fuzzy Kohonen Clustering Network), which combine the character of ensemble with the self-organization of fuzzy Kohonen clustering network.
     分割过程提出了基于GA-FKCN(Genetic Algorithm-Fuzzy Kohonen Clustering Network)的聚类算法,结合了遗传算法的全局性和模糊Kohonen聚类网络的自组织性。
短句来源
     Fuzzy Kohonen clustering network for medullar cell image segmentation
     模糊Kohonen聚类网络用于骨髓细胞图像分割
短句来源
     A New Fuzzy Kohonen Clustering Network
     一种新的模糊Kohonen聚类网络
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     A New Texture Segmentation Method Based on Visual Perception and Improved Fuzzy Kohonen Clustering Network
     一种基于视觉感知特性及改进的模糊Kohonen聚类网络的图象纹理分割方法
     Applications of attribute clustering network and attribute recognition rules in forecasting of futures price trend
     属性聚类网络和属性识别准则在期货价格预测中的应用
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  “聚类网络”译为未确定词的双语例句
     Kohonen Clustering Networks with Fuzzy Selective Multiresolution for Intensity Image Segmentation
     模糊选择多分辨率Kohonen聚类网络用于灰度图像分割
短句来源
     Kohonen Clustering Networks Withfuzzy Selective Multiresolutionfor Intensity Image Segmentation
     模糊选择多分辨率Kohonen聚类网络用于灰度图像分割
     Fuzzy Kohonen cluster network(FKCN) is a fast and effective clustering neural network. It improves Kohonen neural network by using fuzzy membership as its learning rate.
     FKCN(Fuzzy Kohonen cluster netw ork)将模糊隶属度的概念用于Kohonen 神经网络的学习和更新策略中,改善了Kohonen 网络的性能,是一种更为快速有效的聚类网络
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     A novel pattern classification method, named AMC-ASVM, was proposed based on Attribute Mean Clustering and Support Vector Machine.
     在属性均值聚类(AMC)与支持向量机(SVM)的基础上,提出了一个新的模式分类算法——基于(属性)聚类的属性支持向量机算法(AMC-ASVM)。 主要思想是利用属性均值聚类网络得到的具有概率信息(权重)的样本,来训练属性支持向量机,从而得到分类器。
短句来源
     In contrast to most existing fuzzy identification methods, this method uses a hierarchical clustering architecture based on a hybrid neural network for decreasing computational cost of fuzzy identification procedures.
     与现有方法不同,该模糊辨识方法采用自组织神经网络和模糊聚类网络两部分组成的三层神经网络来实现.
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  相似匹配句对
     Networking
     网络
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     NETWORK
     网络
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     MLVQ Clustering Neural Network
     MLVQ网络聚类算法
短句来源
     Topology Cluster Analysis of Network Organization Relationships
     网络组织拓扑聚类分析
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     6. Clus-tering.
     ⑥聚类
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  clustering network
Real-time monitoring of abnormal conditions based on Fuzzy Kohonen clustering network in gas metal arc welding
      
A Fuzzy Kohonen clustering network (FKCN) is put forward to monitor the abnormal conditions in real-time.
      
For the sake of achieving real-time and reducing the computational time costs, the fuzzy Kohonen clustering network (FKCN) is used for modeling and visualization.
      
A hierarchical clustering network based on a model of olfactory processing
      
A set of eukaryotic plant genes have been used to test the validity of the Hamming-Clustering network approach.
      
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potential function based fuzzy clustering network is proposed.On the basis of this,by increasing an output layer and combining the output of hidden membining the output of hidden membership function,a function approxima tion network is realized. The mapping capability of the proposed network is demonstrated by a practical ex ample. The simulation result shows that the approximation capability of the network depends on the num ber of clusters.

提出了一种基于势函数的模糊聚类网络,在此基础上,通过增加一个输出层,综合隐层的隶属函数输出大小,实现一种函数映射网络。通过一个实例验证了该网络的映射功能。实验表明,网络的逼近程度取决于聚类数的多少。

A new fuzzy Kohonen clustering network is proposed which integrates Fuzzy c-Means(FCM)model as the fuzzy control mechanism into the learning rate and updating strategies of Kohonen network.It effec tively overcomes the shortcomings which occur during clustering network parameters are adjusted heuristically The numerical results show improved convergence as well as reduced clustering errors.

本文提出了一种新的模糊Kohonen聚类网络,它将模糊c-均值作为模糊控制机制结合到Kohonen网络学习和更新策略中,有效地解决了经典Kohonen聚类网络中学习参数启发式调整的缺点,仿真结果表明网络的收敛性和分类结果都得到了很大改善.

This paper advances a new approach based on wavelet packets in tandem with the fuzzy cluster neural network. Wavelet packets decompose stationary or nonstationary signals into different bands at different levels. Fuzzy cluster processes the uncertainty problem, and MAXNET realizes selforganizing classification. The approach integrates the advantages of wavelet packets and fuzzy cluster with MAXNET, and therefore it can process uncertainty problems of stationary or nonstationary signals and carry out adaptive...

This paper advances a new approach based on wavelet packets in tandem with the fuzzy cluster neural network. Wavelet packets decompose stationary or nonstationary signals into different bands at different levels. Fuzzy cluster processes the uncertainty problem, and MAXNET realizes selforganizing classification. The approach integrates the advantages of wavelet packets and fuzzy cluster with MAXNET, and therefore it can process uncertainty problems of stationary or nonstationary signals and carry out adaptive pattern recognition and selforganization classification. The network was use to analyse for an example, and the results indicate that it is a useful and effective intelligent classification in the field of fault diagnosis.

基于小波包变换能够把平稳和非平稳信号根据它们的时频特性分解到不同层次上不同频道内,与模糊数学能够处理工程中的不确定性问题,以及MAXNET聚类结构的网络具有自组织聚类分析的优点,提出了小波包模糊聚类网络.该网络能够处理平稳和非平稳信号的不确定性问题,并且具有自适应、自组织聚类分析功能.最后举例说明了该网络在机械诊断实践中是一种行之有效的智能分类器.

 
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