激光与光电子学进展, 2020, 57 (7): 071201, 网络出版: 2020-03-31   

基于PCA-BP神经网络对甲醛和甲醇的识别研究 下载: 1153次

Recognition of Formaldehyde, Methanol Based on PCA-BP Neural Network
作者单位
西北师范大学物理与电子工程学院, 甘肃 兰州 730070
摘要
当前电子鼻对有毒气体的识别存在数据量少,训练生成的神经网络映射能力差等问题。本文以甲醛和甲醇为目标气体,采用自制的气敏传感器对甲醛和甲醇进行数据采集,并对采集到的数据进行滤波和平滑处理,以提取不同传感器对目标气体的响应值。依据准则函数生成伪随机数,并建立伪随机特征值矩阵以扩大有效数据量。利用主成分分析 (PCA)法对特征值进行降维处理,选择贡献率大的主元成分作为反向传播(BP)神经网络的输入向量,构造PCA-BP神经网络。分别用实测特征值矩阵和伪随机特征值矩阵训练PCA-BP神经网络,通过对比分析两个网络得出,实测特征值矩阵的识别率为92%,而伪随机特征值矩阵的识别率为97%。结果表明,伪随机特征值矩阵能有效提高PCA-BP神经网络的映射能力,提高识别正确率。
Abstract
At present, the identification of toxic gases by electronic noses has a small amount of data, and the ability of neural network mapping generated by training is insufficient. In this work, the formaldehyde and methanol are used as target gases, and collected by self-made gas sensor. After filtering and smoothing the collected data, the different response values are extracted. The pseudo-random numbers are generated according to the criterion function, and the pseudo-random matrix is established to expand the effective data volume.The principal component analysis (PCA) is used to reduce the dimensionality of the eigenvalues, and the principal component score with large contribution rate is selected as the input vector of the back-propagation (BP) neural network to construct PCA-BP neural network, which is trained by using the measured eigenvalue matrix and the pseudo-random eigenvalue matrix respectively. By comparing the two networks, the recognition rate of the measured eigenvalue matrix is 92%, and the recognition rate of the pseudo-random eigenvalue matrix is 97%. The results show that the pseudo-random eigenvalue matrix can effectively improve the mapping ability of BP neural network and the accuracy of recognition.

宋海声, 麻林召, 王一帆, 朱恩功, 李承飞. 基于PCA-BP神经网络对甲醛和甲醇的识别研究[J]. 激光与光电子学进展, 2020, 57(7): 071201. Haisheng Song, Linzhao Ma, Yifan Wang, Engong Zhu, Chengfei Li. Recognition of Formaldehyde, Methanol Based on PCA-BP Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(7): 071201.

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