Title :
On the Use of the SVM Approach in Analyzing an Electronic Nose
Author :
Gaudioso, Manlio ; Khalaf, Walaa ; Pace, Calogero
Author_Institution :
Univ. della Calabria, Rende
Abstract :
We present an Electronic Nose (ENose) which is aimed both at identifying the type of gas and at estimating its concentration. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnOz) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH-3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware-software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, and then to estimate its concentration, respectively. In particular we adopt a training model using the Support Vector Machine (SVM) approach to teach the system how discriminate among different gases, then we apply another training model using the least square regression, for each type of gas, to predict its concentration.
Keywords :
electronic noses; humidity sensors; learning (artificial intelligence); pressure sensors; support vector machines; temperature sensors; electronic nose; gas sensors; humidity sensor; integrated hardware-software system; least square regression principle; machine learning principles; pressure sensor; support vector machine approach; temperature sensor; Electronic noses; Gas detectors; Humidity; Least squares approximation; Machine learning; Predictive models; Sensor systems; Support vector machines; Temperature sensors; Tin;
Conference_Titel :
Hybrid Intelligent Systems, 2007. HIS 2007. 7th International Conference on
Conference_Location :
Kaiserlautern
Print_ISBN :
978-0-7695-2946-2
DOI :
10.1109/HIS.2007.16
