电子鼻中预处理算法选择及阵列优化

Preprocessing Algorithm Selection and Optimization of Sensor Array in Electronic Noses

为了研究数据预处理算法和传感器阵列优化对电子鼻气体辨识的影响,对3种气体进行了测试。使用主成分分析(Principal component analysis,PCA)法选择预处理算法,确定分类效果最好的相对差分法对电子鼻数据进行预处理。对初始阵列优化前,首先通过传感器响应变化趋势及变异系数剔除响应异常的传感器;然后进行PCA因子载荷分析,结合相关系数分析及方差膨胀因子进行多重共线性检验确定可能的最优阵列。最后,运用反向传播(Back propagation,BP)神经网络对可能的最优阵列进行气体识别检验并确定最终阵列,同时选取其他阵列作为对照研究。通过计算检验,证明本文的阵列优化方法不仅可以剔除异常和冗余传感器,而且对测试样本分类效果良好。

Three gases are tested to investigate the effects of data preprocessing algorithm and optimization of sensor array on electronic noses.Preprocessing algorithms are chosen via principal component analysis（PCA）,and the relative difference algorithm is determined for preprocessing data of the electronic nose for its good classification effect.To optimize the initial array,we first remove sensors abnormally responsing by observing the sensors′response trend and coefficient of variation.Then we analyze PCA factor loading and conduct multi-collinearity test to determine possible optimal arrays using the correlation coefficient and variance inflation factor analysis.Finally,we apply back propagation（BP）neural network to verify the possible optimal arrays through gas recognition.We determine the final array as well as select other array for controlled study.The results of the check computation certify that the optimization method of sensor array can not only eliminate anomalies and redundant sensors,but also works well on the classification of test samples.