作　　者： ; ; ; ;

机构地区： 华中科技大学机械科学与工程学院

出　　处： 《机械工程学报》 2005年第2期46-50,共5页

摘　　要： 讨论了一种SOM网络训练结果的可视化技巧,结合该技巧提出了基于SOM网络的特征选择方法。该方法 通过计算出SOM网络竞争层神经元权值中各维特征对输入模式聚类识别的影响,可以选择出对于模式识别敏感 的特征集。用IRIS和齿轮故障数据对该方法进行了检验,研究结果表明,采用该方法能较好地从原始特征中选择 出有效特征子集,实现不同类别输入数据之间的模式聚类识别。 A technique to visualize the trained self-organizing maps (SOM) networks results is discussed, and an approach for feature selection based on SOM networks combining with the visualization technique is presented. In the approach, the responsibilities of every dimensional feature in SOM networks competitive neurons weights to the input data are computed, and then the feature sets being, sensitive to pattern recognition, which have the main responsibilities, are selected accordingly. The experimental data sets including the well-known IRIS data and gearbox failure data are used to test the approach. It is proved from the investigation that efficient feature sets are chosen easily from raw feature sets, and then pattern recognition of input data is realized.

关 键 词： 网络 特征选择 模式聚类 特征集 模式识别 输入 权值 法能 研究结果 神经元

领　　域： [机械工程] [自动化与计算机技术] [自动化与计算机技术]