Research of Electronic Nose Pattern Recognition Algorithm Based on SVM

In order to improve the recognition rate of the electronic nose system for small samples, an electronic nose pattern recognition algorithm based on support vector machine (SVM) is proposed in this paper. Identification experiments for three kinds of wine with similar odor were carried out. The sensor arrays are optimized by means of principal component analysis (PCA) method first. Then, make comparing experiment using different algorithms for different number of training samples of wine. The related results show that PCA-SVM based pattern recognition algorithms has high recognition accuracy, stronger classification capability, and has potential advantages in small sample classification and recognition experiments.