基于SOM特征映射空间相似度判别的软传感器建模变量选择
Variable Selection for Soft-Sensing Model Based on False Nearest Neighbors in Self-Organizing Feature Mapping Feature Space
摘要: 针对软传感器建模中存在的信息冗余，提出一种基于自组织特征映射神经网络(Self-Organizing Feature Mapping，SOM)的变量选择方法。该方法借助SOM简单快速的特征映射能力对数据进行投影，采用虚假最近邻点法(False Nearest Neighbor，FNN)计算某变量删减前后数据在SOM投影空间的相似度，通过相似度来判断其对主导变量的解释能力，由此进行变量的选择。实验结果表明该方法能有效的进行变量选择，为软传感器建模变量选择提供了一种新思路。
Abstract: A new variable selection method based on Self-Organizing Feature Mapping (SOM) is proposed for soft-sensing modeling to eliminate redundant information. In the proposed method, SOM is used to get new space from original variable space because of its simple and fast. The False Nearest Neighbor (FNN) is used to calculate the similarity of data in the new SOM space. The primary variable would be estimated to select secondary variables. The results show that the method is effective and suitable for variable selection. Therefore, a new method is provided for the variable selection of soft-sensing modeling.
文章引用：侯杰, 李太福, 余德君, 程杨. 基于SOM特征映射空间相似度判别的软传感器建模变量选择[J]. 运筹与模糊学, 2011, 1(1): 16-21. http://dx.doi.org/10.12677/orf.2011.11004

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