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.
CHEN Hong-yan,LIU Jia-hao,SHENG Wei-ming, et al. Application of SVM Based on Grey Wolf Optimizer in Measurement Error Analysis of Infrared Methane Sensor[J]. Acta Metrologica Sinica, 2021, 42(9): 1244-1249.
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2021, Vol. 42 
