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.
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.
文中首先对特征映射理论进行了详细的分析，建立了系统的应用信息功能模型，对系统集成的接口进行了详细的设计，提出了直接映射与间接映射相结合的复合映射模式。
The theory of feature mapping is analyzed in detail. The functional model of applied information is created and the design of data access interface is designed in detail in this dissertation.
文中首先对特征映射理论进行了详细的分析，建立了系统的应用信息功能模型，对系统集成的接口进行了详细的设计，提出了直接映射与间接映射相结合的复合映射模式。
The theory of feature mapping is analyzed in detail. The functional model of applied information is created and the design of data access interface is designed in detail in this dissertation.
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