Recognition of Formaldehyde, Methanol Based on PCA-BP Neural Network

Haisheng Song; Linzhao Ma; Yifan Wang; Engong Zhu; Chengfei Li

doi:10.3788/LOP57.071201

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 7 >Page 071201 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 7, 071201 (2020)

Recognition of Formaldehyde, Methanol Based on PCA-BP Neural Network

Haisheng Song, Linzhao Ma^*, Yifan Wang, Engong Zhu, and Chengfei Li

Author Affiliations

College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China

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DOI: 10.3788/LOP57.071201 Cite this Article Set citation alerts

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 Copy Citation Text

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Fig. 1. Experimental system block diagram

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Fig. 2. Gas identification process

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Fig. 3. Acquisition of data waveform. (a) Raw data waveform; (b) filtered data waveform

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Fig. 4. PCA-BP neural network model

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Fig. 5. PCA score chart. (a) First two main components; (b) first three main components

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Fig. 6. Classification results of BP neural network. (a) A-PCA-BP classification; (b) W-PCA-BP classification

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Senor		Formaldehyde /μL			Methanol /μL
Senor		2	4	6	2	4	6
1	σμ	0.0666	0.0621	0.0441	0.0031	0.0027	0.0021
1	σμ	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
2	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
3	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
4	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
5	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
6	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
7	μ	3.4741	4.1141	5.4641	3.0595	3.1495	5.1293

Table 1. Relative standard deviation of gas sensor response

τ_i	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 2. Data similarity of partial W matrix

Sampletype	Studysamples /piece	Recognitionresult /piece	Identificationerror /%
Formaldehyde	30	31	3.3
Methanol	30	29	3.3

Table 3. Recognition results of matrix A on W-PCA-BP network

Method	Formaldehydeidentification samples	Methanol identificationsamples	Total numberof samples	Number oferror /piece	Processingtime /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 4. Identification of formaldehyde and methanol bymatrix A trained network

Method	Formaldehydeidentification samples	Methanol identificationsamples	Total numberof samples	Number oferror /piece	Processingtime /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

Table 5. Identification of formaldehyde and methanol bymatrix W trained network

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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