粒子群优化算法在混合气体红外光谱定量分析中的应用

Application of Particle Swarm Optimization Algorithm in Infrared Spectrum Quantitative Analysis of Gas Mixture

通过将粒子群优化技术及BP神经网络技术相结合,建立了三种烃烷混合气体的红外光谱定量分析模型。混合气体主要由甲烷、乙烷、丙烷三种组分气体组成,三种组分气体浓度范围分别为0.01%～0.1%。文章首先采用主成分分析技术从红外光谱1866个数据中提取了5个特征变量作为神经网络的输入,将气体浓度作为网络输出。然后将粒子群优化算法与BP神经网络技术相结合,对网络的隐含层节点数进行了优化选择。再对结构优化后的网络进行训练,建立气体分析模型。分析模型的标准气体验证实验结果表明,采用此方法建立混合气体红外光谱定量分析模型所用时间(大约4600s)比单纯采用BP神经网络进行遍历优化建模所用时间(大约24500s)降低5倍以上,模型预测精度水平相当,网络结构大致相同,具有一定的实践意义和应用潜力。

An infrared spectrum quantitative analysis model was built based on particle swarm optimization algorithm （PSO）and backward propagation （BP）neural network. This model aimed at three components of gas mixture, with methane, ethane and propane gases included. The concentration of each component ranged from 0. 01 % to 0. 1 %. Five features variables were abstracted from 1 866 infrared spectrum data by principal component analysis as the input of the BP network. The gas concentrations acted as the output. PSO was used to optimize the number of neural network hidden layer nodes. Then, the network was trained to construct models for quantitative analysis of these three kinds of gas. The experiment results show that the time taken for optimizing the prediction model by PSO, about 4 600 second, reduced to one fifth of that of ergodic optimizing, which is about 24 500 second. The precision of the model is corresponsive and the structure of the network is approximately the same. So the PSO has definite practical significance and application potential.