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Citation: HU Lan-Ping, GE Cun-Wang, CHEN Ting-Ting, SHI Chuan-Guo. Application of PCA-LVQ in Processing of RS-FTIR Data for Atmospheric Environment Monitoring[J]. Chinese Journal of Applied Chemistry, ;2007, 24(12): 1364-1367. shu

Application of PCA-LVQ in Processing of RS-FTIR Data for Atmospheric Environment Monitoring

1.
School of Chemistry and Chemical Engineering, Nantong University, Nantong 226006

Corresponding author: HU Lan-Ping,
Received Date: 14 January 2007
Available Online: 14 April 2007
Fund Project: 国家自然科学基金(60571055) (60571055)南通市科技项目基金(K2006007)资助项目 (K2006007)

Principle component analysis(PCA),in combination with learning vector quantization(LVQ) neural network,was applied to extract information features from remote sensing Fourier transform infrared spectroscopic(RS-FTIR) data,and rapid qualitative analysis of eight components in atmosphere was demonstrated using this PCA-LVQ approach.Compared with LVQ neural network and BP-ANN,PCA-LVQ can process data more efficiently,with a much faster computation speed and a higher prediction accuracy.The classification precision was as high as 91.7%.The high prediction accuracy and fast computation speed of PCA-LVQ can meet the needs of real-time and on-line monitoring of atmospheric toxic species via FTIR.
- principal component analysis(PCA),
- LVQ neural network,
- BP-ANN,
- multi-component analysis,
- environmental monitoring
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