Laser & Optoelectronics Progress, Volume. 57, Issue 7, 071201(2020)
Recognition of Formaldehyde, Methanol Based on PCA-BP Neural Network
Figures & Tables(11)
Fig. 3. Acquisition of data waveform. (a) Raw data waveform; (b) filtered data waveform
Fig. 5. PCA score chart. (a) First two main components; (b) first three main components
Fig. 6. Classification results of BP neural network. (a) A-PCA-BP classification; (b) W-PCA-BP classification
Table 1. Relative standard deviation of gas sensor response
View tableTable 1. Relative standard deviation of gas sensor response
Senor Formaldehyde /μL Methanol /μL 2 4 6 2 4 6 1 σ μ 0.0666 0.0621 0.0441 0.0031 0.0027 0.0021 3.2691 3.6191 4.9390 3.9663 4.3863 5.9063 2 σ 0.0116 0.0102 0.0074 0.0048 0.0036 0.0027 μ 3.9011 4.4411 6.1211 3.5445 4.7445 6.3245 3 σ 0.0071 0.0066 0.0066 0.0039 0.0025 0.0018 μ 4.5214 4.8714 6.0893 2.8882 4.5382 6.3982 4 σ 0.0115 0.0096 0.0096 0.0056 0.0041 0.0031 μ 3.8311 4.5811 6.6411 3.1325 4.3825 5.8625 5 σ 0.0112 0.0087 0.0074 0.0099 0.0086 0.0064 μ 3.5972 4.5972 5.4572 3.7638 4.3438 5.8338 6 σ 0.0161 0.0131 0.0107 0.1048 0.0669 0.0669 μ 3.4628 4.3128 5.2388 0.9534 1.4934 2.0161 7 σ 0.0261 0.0221 0.0165 0.3765 0.3369 0.3369 μ 3.4741 4.1141 5.4641 3.0595 3.1495 5.1293
Table 2. Data similarity of partial W matrix
View tableTable 2. Data similarity of partial W matrix
τi 1 2 3 4 5 6 7 1 1 0.2455 -0.3589 -0.4109 0.1243 0.1406 0.3054 2 0.2455 1 -0.0841 0.0651 -0.4414 0.1488 0.5628 3 -0.3589 -0.0841 1 0.2956 -0.2973 0.0211 -0.3623 4 -0.4109 0.0651 0.2956 1 0.0373 -0.1307 0.0462 5 0.1243 -0.4414 -0.2973 0.0373 1 -0.0602 0.0502 6 0.1406 0.1488 0.0211 -0.1307 -0.0602 1 0.0583 7 0.3054 0.5628 -0.3623 -0.0462 0.0502 0.0583 1
Table 3. Recognition results of matrix A on W-PCA-BP network
View tableTable 3. Recognition results of matrix A on W-PCA-BP network
Sample type Study samples /piece Recognition result /piece Identification error /% Formaldehyde 30 31 3.3 Methanol 30 29 3.3
Table 4. Identification of formaldehyde and methanol bymatrix A trained network
View tableTable 4. Identification of formaldehyde and methanol bymatrix A trained network
Method Formaldehyde identification samples Methanol identification samples Total number of samples Number of error /piece Processing time /s A-BP 38 22 60 8 3.2 W-BP 100 100 200 8 3.5 A-PCA-BP 35 25 60 5 3.0 W-PCA-BP 94 106 200 6 3.3
Table 5. Identification of formaldehyde and methanol bymatrix W trained network
View tableTable 5. Identification of formaldehyde and methanol bymatrix W trained network
Method Formaldehyde identification samples Methanol identification samples Total number of samples Number of error /piece Processing time /s A-BP 27 33 60 3 4.5 W-BP 102 98 100 2 5.0 A-PCA-BP 31 29 60 1 2.8 W-PCA-BP 95 105 200 5 3.1
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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
Paper Information
Category: Instrumentation, Measurement and Metrology
Received: Oct. 9, 2019
Accepted: Nov. 26, 2019
Published Online: Mar. 31, 2020
The Author Email: Ma Linzhao (1093704655@qq.com)
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