Research on Concentration Retrieval of Gas FTIR Spectra by Interval Extreme Learning Machine and Genetic Algorithm
CHEN Yuan-yuan1, 3, WANG Zhi-bin1, 2, 3, WANG Zhao-ba1, 2 , LI Xiao3
1. State Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China 2. Key Lab of Instrumentation Science & Dynamic Measurement(North University of China), Ministry of Education, Taiyuan 030051, China 3. Engineering Technology Research Center of Shanxi Province for Opto-Electronic Information and Instrument, Taiyuan 030051, China
Abstract：This paper proposed a novel effective quantitative analysis method for FTIR spectroscopy of polluted gases, which select the best wavenumbers based on the idea of interval dividing. Meanwhile, genetic algorithm was adopted to optimize the connect weights and thresholds of the input layer and the hidden layer of extreme learning machine (ELM) because of its global search ability. Firstly, the whole spectrum region was divided into several subintervals; Secondly, the quantitative analysis model was established in each subinterval by using optimized GA-ELM; Thirdly, the best combination of subintervals was selected according to the generalized performance of each subinterval model by computing the parameters root mean square error (RMSE) and determined coefficients r. In this paper, the mixture of CO, CO2 and N2O gases were selected as the research object and the whole spectrum range was from 2 140 to 2 220 cm-1. The experiment results showed that the RMSE of model established with the selected wavenumbers was 154.996 3, the corresponding r can reach 0.987 4, and the running time was just 0.8 seconds, which indicated that the concentration retrieval model established with the proposed Interval-GA-ELM (iGELM) method can not only reduce the modeling time, but also can improve the stability and predict accuracy, especially under the condition of the exist of interferents, which providing an effective approach to the remote analysis of polluted gases.
Key words：Interval dividing;Extreme learning machine;Genetic algorithm;Concentration retrieval of gas FTIR spectra
陈媛媛1,3，王志斌1, 2, 3，王召巴1, 2，李 晓3 . 区间极限学习机结合遗传算法用于红外光谱气体浓度反演的研究 [J]. 光谱学与光谱分析, 2014, 34(05): 1244-1248.
CHEN Yuan-yuan1, 3, WANG Zhi-bin1, 2, 3, WANG Zhao-ba1, 2 , LI Xiao3 . Research on Concentration Retrieval of Gas FTIR Spectra by Interval Extreme Learning Machine and Genetic Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(05): 1244-1248.
[1] Nakagawa H, Tajima T, Kano M, et al. Anal. Chem., 2012, 84(8): 3820. [2] Wilk A, Seichter F, Kim S S, et al. Anal. Bioanal. Chem., 2012, 402(1): 397. [3] Serena S, Maria P, Kipchirchir B. Appl. Spectrosc., 2013, 67(9): 1019. [4] Borges G, Rohwedder J, Bortoni E. IEEE Instrumentation and Measurement Technology Conference(12MTC) Minneapolis, USA, 2013, 1666. [5] Bai Peng, Ji Juanzao, Liu Peng, et al. Applied Mechanics and Materials, 2012, 128: 557. [6] Vapnik V, Levin E, Cun Yannle. Neural Computation, 1994, 6(5): 851. [7] Argyri A A, Jarvis R M, Wedge D. Food Control, 2013, 29(2): 461. [8] Huang Guangbin, Siew C K. International Joint Conference on Neural Networks, Kunming, China, 2004. 1029. [9] Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al. IEEE Trans. Syst. Man Cybern. B Cybern., 2012, 42(2): 513. [10] Niazi A, Leardi R. J. Chemom., 2012, 26(6): 345. [11] Rothman L S, Gordon I E, Barbe A, et al. Journal of Quantitive Spectroscopy and Radiative Transfer, 2009, 110(9-10): 533. [12] ZHANG Hua, SHI Guang-yu(张 华, 石广玉). Chin. J. Atmos. Sci.(大气科学), 2000, 24(1): 111.
