科研之友
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摘要
According to the difficulty in selecting parameter of least square support vector machine (LS-SVM) when modeling on the gas mixture, and the high computational complexity of the infrared spectrum data, LS-SVM optimized by particle swarm optimization (PSO) algorithm was proposed to build an infrared spectrum quantitative analysis model with feature extracted by principal component analysis (PCA). Firstly, seven feature variables were extracted by PCA as the input of the model from 550 infrared spectrum data of the main absorption apex field, so the computational complexity was reduced. This model aimed at three components of gas mixture. in which methane. ethane and propane gases are included. The concentration of each component ranged from 0. 1%, to 1%, 0. 1% to 1% and 0. 1% to 1. 5% respectively. Each component quantitative analysis model was built by LS-SVM and the parameters were optimized by PSO algorithm, then the regression model would be reconstructed according to the optimal parameters. This method replaced the traditional ergodic optimization. The experiment results show that the time of offline modeling by PSOwas reduced to one fortieth of that of ergodic optimizing. The precision of the model was corresponsive. It can meet the requirement of the Measure. PSO algorithm has more superior performance on global optimization and convergence speed. So it is feasible to combine PSO algorithm with LS-SVM to create the infrared spectrum quantitative analysis model. It has definite practice significance and application value.
