A method that applies the clustering function of SOFM (Self-Organizing Feature Maps) network is proposed for autonomous star pattern recognition.
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。
An autonomous star pattern recognition method using the tri-star clustering function of SOFM (Self-Organizing Feature Maps) network is described.
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。
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.
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 self-organizing feature map (SOFM) uses weight of network to present structure of the input data and has preferable ability of classification.
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。
An image fusion binarization method based on Selforganization Feature Map (SOFM) neural network is presented.
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。
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.
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。
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