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.
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。
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。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
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。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
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。
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.
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。
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。
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。
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