基于SVM和PCA的痕量多组分气体检测方法

摘要

Gas sensors and optical sensors are difficult to detect trace multi﹣component gases. In this paper, a detection method of fast chromatography combined with gas sensor array is introduced to obtain the characteristic signal of trace multi﹣component gases. Support vector machine ( SVM ) is introduced to classify the samples according to the features. Then, to obtain a better gas identification model, particle swarm optimization algorithm is utilized to optimize the parameters of SVM. Based on actual sample detection and recognition, and compared to detection method by similar testing instrument, the proposed method has better selectivity of mixed gases. Using SVM、 PCA and PSO method is more suitable for processing and identification of small sample data. Developed multi﹣component gas detection prototype has better recognition rate, repeatability and stability.%针对气敏和光学传感器等常规方法难以检测痕量多组分气体的问题，采用快速色谱与气敏传感器阵列结合的检测方法获取痕量多组分气体的信号，然后对信号采用支持向量机( SVM)训练分类气体模式特征。为获得较好的气体识别模型，使用粒子群算法( PSO)优化SVM参数。通过对实际样品检测和识别，并对比评估同类检测仪器采用的检测识别方法，验证文中方法对混合气体的选择性更好，采用的SVM、PCA和PSO组合方法更适合处理和识别小样本数据。研制的多组分痕量气体检测样机具有更高的识别率、重复性和稳定性。