一种基于隐变量模型的聚类算法用于气体传感器阵列数据的模式识别

Pattern recognition of gas sensor arrary data by using a clustering algorithm based on latent variable model

聚类分析方法是一种无需先验信息即能探索数据内在分类结构信息的模式识别方法,已经被广泛应用到气体传感器阵列的模式识别研究中。该文提出了基于隐变量模型的聚类算法对两组金属氧化物半导体(MOS)传感器阵列数据进行模式识别。数据处理结果表明,该方法能准确的对两组传感器阵列数据中对应不同气体物质的样本进行分类识别。

Cluster analysis is a kind of method that can explore the underlying group information of data without any aid of the a priori information about the data. It has been widely employed for the pattern recognition of the gas sensor array. In the present study, we use a clustering algorithm based on the latent variable modeling, which is proposed by the author, to recognize the patterns in two metal oxide semiconductor (MOS) gas sensor array data set. The results obtained after the procession of the data show that the algorithm can inerrable identify the samples according to different gas chemical substances in the two data set.