Signals recognition of electronic nose based on support vector machines

A new intelligent method for signals recognition of electronic nose, based on support vector machine (SVM) classification, is presented. The SVM operates on the principle of structure risk minimization; hence a better generalization ability is guaranteed. This paper discusses the basic principle of the SVM at first, and then uses it as a classifier to recognize the gas category. The method can classify complicated patterns and achieve higher recognition rate at reasonably small size of training sample set and can overcome disadvantages of the artificial neural networks. The experiments of the recognition of three different gases, ethanol, gasoline and acetone, have been presented and discussed. The results indicate that the SVM classifier exhibits good generalization performance and enables the average recognition rate to reach 88.33% for the testing samples. This means the method proposed is effective for signals recognition of electronic nose.