In this paper, the Self Organizing Map(SOM) ( SOM ) learning and classification algorithms are modified. 本文改进了自组织映射学习和分类算法。
Self Organizing Map(SOM) 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。
According to self organizing map principle, a visualized topology map model for radar system effectiveness analysis based on information fusion of multiple parameters is built, and the effectiveness of different airborne radar is analyzed through the topology map obtained from the model. 根据自组织神经网络原理,构建了基于多参数信息融合的雷达系统效能可视拓扑映射图分析模型,并根据模型学习获得的拓扑映射图对机载雷达效能进行了分析。
Self Organizing Map(SOM) Neural Network ( SOM ) that has the capability of classifying automatically is used to classify the reclamation condition, which provides the base for adopting suitable reclamation method. 介绍了利用自组织映射神经网络的自动分类功能对进行矿区土地复垦条件分类,为因地制宜地采取复垦措施提供依据。
The Analysis of Feature Principal Component Extraction and Self organizing Map 特征主元提取与自组织影射的剖析