1. College of Modern Science and Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. College of Mechanical & Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
Abstract：A SVM regression model based on grey wolf optimization (GWO) algorithm was proposed to solve the problem of low prediction accuracy when the traditional support vector machine (SVM) regression model was applied to measurement data processing of infrared methane sensor. Based on the traditional support vector machine, the model used the grey wolf optimization algorithm to adaptively search the feature space to select the best feature combination. After cyclic comparison, the model could quickly and accurately search for the optimal penalty factor C and gamma parameters. After the measurement of standard methane gas in the concentration range of 0～5.05% with the infrared methane sensor developed in the laboratory, three SVM regression models were established and compared. The results showed that the support vector machine regression model established by the grey wolf optimization algorithm had the smaller absolute and relative errors and the higher accuracy.
［1］Zheng Y, Dang P P, Zheng C T, et al. A handheld mid-infrared methane sensor using a dual-step differential method for additive/multiplicative noise suppression［J］. Infrared Physics and Technology, 2016, 79(10): 151-159.
［2］Triki M, Ba T N, Vicet A. Compact sensor for methane detection in the mid infrared region based on Quartz Enhanced Photoacoustic Spectroscopy［J］. Infrared Physics and Technology, 2015, 69(1): 74-80.
［3］Gulbag A, Temurtas F, Tasaltin C, et al. A study on radial basis function neural network size reduction for quantitative identification of individual gas concentrations in their gas mixtures［J］. Sensors and Actuators B: Chemical, 2007, 124(2): 383-392.
［4］杨景明, 郭秋辰, 孙浩, 等. 基于改进果蝇算法与最小二乘支持向量机的轧制力预测算法研究［J］. 计量学报, 2016, 37(5): 505-508.
Yang J M, Guo Q C, Sun H, et al. Research on the Predictive Algorithm of Rolling Force Based on the Improved Fruit Flies Optimization Algorithm with LSSVM［J］. Acta Metrologica Sinica, 2016, 37(5): 505-508.
［5］吴永忠, 程文娟, 韩江洪, 等. 一种新型的红外甲烷测量定量分析模型［J］. 煤炭学报, 2009, 34(2): 275-279.
Wu Y Z, Cheng W J, Han J H, et al. A Quantitative Analysis Model to Determine Methane Concentration by Infrared Absorbance Method［J］. Journal of China Coal Society, 2009, 34(2): 275-279.
［6］梁杰, 李庆超. 车载气体传感器测量结果修正算法研究［J］. 计量学报, 2019, 40(6): 1140-1145.
Liang J, Li Q C. Research on Correction Algorithm of Vehicle Gas Sensor Measurement［J］. Acta Metrologica Sinica, 2019, 40(6): 1140-1145.
［7］Burges J C C. A tutorial on support vector machine for pattern recognition［J］. Data Mining and Knowledge Discovery, 1998, 2: 121-167.
［8］Vapnik V. The nature of statistical learning theory［M］. New York: Springer, 1999.
［9］Mirjalili S, Mirjalili S M, Lewis A. Grey Wolf Optimizer［J］. Advances in Engineering Software, 2014, 69(3): 46-61.
［10］Kamboj V K, Bath S K, Dhillon J S. Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer［J］. Neural Computing and Applications, 2016, 27(5): 1301-1316.
［11］Dietachmayr F W, Winkler A W, Zagar B G. Model based analysis of non-dispersive infrared gas sensors with thermal emitters and thermopile detectors［J］. Tm-Technisches Messen, 2018, 85: 221-232.
［12］Tan Q L, Tang L C, Xue C, et al. Three-gas detection system with IR optical sensor based on NDIR technology［J］. Optics and Lasers in Engineering, 2015, 74(12): 103-108.
［13］鲍立, 陈红岩, 郭晶晶, 等. 基于改进PSO算法的SVM在甲烷测量中的应用［J］. 传感技术学报, 2017, 30(9): 1454-1458.
Bao L, Chen H Y, Guo J J, et al. Application of SVM Based on Improved Particle Swarm Optimization Algorithm in Methane Measurement［J］. Chinese Journal of Sensors and Actuators, 2017, 30(9): 1454-1458.
［14］Bian X Q, Zhang L, Du Z M, et al. Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine［J］. Journal of Molecular Liquids, 2018, 261(4): 431-438.
［15］Bian X Q, Zhang Q, Zhang L, et al. A grey wolf optimizer-based support vector machine for the solubility of aromatic compounds in supercritical carbon dioxide［J］. Chemical Engineering Research and Design, 2017, 123(5): 284-294.
2021, Vol. 42 
