提出一种量子自组织特征映射网络模型及聚类算法。
A quantum self-organization feature mapping networks model and its clustering algorithm are presented.
本文讨论了基于自组织特征映射网络聚类算法的基本原理，并指出了算法的缺陷。
In this paper, the basic principle of the clustering algorithm based on self-organizing feature map network is discussed, and pointed out its defects.
本文研究了多层感知器、径向基函数网络、学习向量量化网络和自组织特征映射网络等四种神经网络在回转窑火焰图像分割中的应用。
In this paper, four neural networks, i. e. multi layer perception, radial basis function, learning vector quantization and self organizing feature mapping, are used to segment the flame image.
针对汽轮机转子故障分类问题，采用模糊数学和自组织特征映射神经网络方法诊断汽轮机转子的故障。
In the light of the problems involved in a steam turbine rotor fault diagnosis proposed in this paper is a new diagnostic method based on a fuzzy self organizing neural network.
本文简要介绍了自组织特征映射神经网络的基本原理，并利用其原理对土地复垦的条件分类进行了初步研究。
The paper briefly introduces the fundamentals of neural network of self-organizing feature map and on the basis of which discusses the classification of land reclamation conditions.
自组织特征映射是一种人工神经网络方法，可以同时实现模式识别和数据分类。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
介绍了一种利用自组织特征映射(SOFM)网络的聚类功能进行全天星图识别的方法。
A method that applies the clustering function of SOFM (Self-Organizing Feature Maps) network is proposed for autonomous star pattern recognition.
本文根据自组织特征映射神经网络学习算法，提出了其权值的CMOS实现电路。
According to a learning algorithm of self organizing neural network for mapping character, a CMOS implementation of its synaptic weight by circuit is presented in this paper.
介绍了一种利用SOFM(自组织特征映射)网络的聚类功能进行全天星图识别的算法。
An autonomous star pattern recognition method using the tri-star clustering function of SOFM (Self-Organizing Feature Maps) network is described.
本文通过利用涌现自组织特征映射神经网络对数据进行聚类分析，并通过无边界u矩阵实现可视化功能。
To facilitate clustering analysis and visualization of data, the Emergent Self-Organizing Feature Maps (ESOM) and a boundless U-matrix are needed.
本文应用多层前馈神经网络和自组织特征映射神经网络分别对简单目标和复杂飞机目标进行了分类识别。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
自组织特征映射人工神经网络对正常肝的识别正确率达84.8% ，对脂肪肝的识别正确率达90 .9%。
The neural network algorithm showed accuracy rate of 84.8% for normal liver and 90.9% for fatty liver.
自组织特征映射(SOM)神经网络能通过自组织有效地提取出各特征参数间的内在特征并映射到分类模板上，它可以用于各种模式识别问题。
Self organizing feature map (SOM) network can extract the internal features of parameter by self organizing and reflect them on the classified map. It can be used in problems of pattern recognition.
对自组织特征映射神经网络的特性进行分析，并将其与矢量量化问题的实质进行比较，提出了一个实现矢量量化的自组织特征映射算法。
The characteristics of SOFM neural network is analysed and compared with the feature of Vector Quantizing problem in this paper. Based on this an algorithm for Vector Quantizing is put forward.
自组织特征映射（SOFM）网络利用神经元权值向量表示输入数据的结构、具有较好的分类能力。
The self-organizing feature map (SOFM) uses weight of network to present structure of the input data and has preferable ability of classification.
提出了一种基于自组织特征映射(SOFM)神经网络的图像融合二值化方法。
An image fusion binarization method based on Selforganization Feature Map (SOFM) neural network is presented.
第一层结构使用自组织特征映射神经网络（SOFM）将像素映射到二维的平面上。
The first level of our system employs the self-organizing feature map (SOFM) to map colors of image on a two dimensional feature map.
第一层结构使用自组织特征映射神经网络（SOFM）将像素映射到二维的平面上。
The first level of our system employs the self-organizing feature map (SOFM) to map colors of image on a two dimensional feature map.
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