基于PSO-SVM的白酒品质鉴别电子鼻

Liquor recognition electronic nose based on PSO-SVM

研制一套白酒品质识别电子鼻,对检测样品的气味数据进行预处理,提取稳态响应值,并作为支持向量机(support vectormachine,SVM)分类模型的输入。为提高识别的准确度,利用粒子群算法(particle swarm optimization,PSO)来优化SVM的参数c和g。不同品质的白酒所对应的电子鼻传感器响应特性不同,PSO-SVM分类模型的识别准确率达到了97.5%。结果证明基于PSO-SVM分类模型具有较强的学习能力和泛化能力,可用于白酒品质鉴别电子鼻。

In this paper, the classification model of support vector machine （SVM） for liquors was established. To improve the accuracy of typical samples, the modeling parameters c and g for SVM were optimized by particle swarm optimization （PSO）. The simulative results indicated that the classification accuracy of four kinds of liquors samples which were acquired by electronic nose reached 97.5% based on PSO-SVM. The liquors recognition electronic nose had different characteristic response signals for them, which could recognize Yanghe, Jinshiyuan, Shuangge and Erguo- tou. The PSO-SVM model has good ability both in learning and generalization, and the algorithm can be effectively used in liquors recognition by electronic nose.