In this paper we present a novel method for odour recognition based on a supervised fuzzy C-means (SFCM) algorithm and a k-nearest neighbour (k-NN) algorithm. The method is applied to experimental data collected from a sensor array composed of metal oxide sensors (MOSs). The sensors are exposed to odourants and the relative resistance values are used for classification. SFCM selects the features, which better characterize the sensor responses, and computes both the memberships of the odourants to classes, and the shape of classes. k-NN exploits the output of SFCM to recognize unknown odourants. We describe the application of the method to the classification of food packages and show that the experimental results support the methodology presented.
Recognition of olfactory signals based on supervised fuzzy c-means and k-NN algorithms
MARCELLONI, FRANCESCO
2001-01-01
Abstract
In this paper we present a novel method for odour recognition based on a supervised fuzzy C-means (SFCM) algorithm and a k-nearest neighbour (k-NN) algorithm. The method is applied to experimental data collected from a sensor array composed of metal oxide sensors (MOSs). The sensors are exposed to odourants and the relative resistance values are used for classification. SFCM selects the features, which better characterize the sensor responses, and computes both the memberships of the odourants to classes, and the shape of classes. k-NN exploits the output of SFCM to recognize unknown odourants. We describe the application of the method to the classification of food packages and show that the experimental results support the methodology presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
