Izvorni znanstveni članak
Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors
Lin Zhao
; School of Electronic Science and Technology, Dalian University of Technology, Dalian, P.R. China; School of Computer Science and Technology, Dalian Neusoft University of Information, Dalian, P.R. China
Jing Wang
; School of Electronic Science and Technology, Dalian University of Technology, Dalian, P.R. China
Xiaogan Li
; School of Electronic Science and Technology, Dalian University of Technology, Dalian, P.R. China
Sažetak
Sensor array with pattern recognition method is often used for gas detection and classification. Processing time and accuracy have become matters of widespread concern in using data analysis with semiconductor gas sensor array for volatile organic compound gas mixture classification. In this paper, a sensor array consisting of four nanostructured semiconductor gas sensors was used to generate the response signal. Three main categories of gas mixtures, including single-component gas, binary-component gas mixtures, and four-component gas mixtures, are tested. To shorten the training time, extreme learning machine (ELM) is introduced to classify the category of gas mixtures and the concentration level (low, middle, and high) of formaldehyde in the gas mixtures. Our results demonstrate that, compared to traditional neural networks and support vector machines (SVM), ELM networks can achieve 204 and 817 times faster training speed. As for classification accuracy, ELM networks can achieve comparable results with SVM.
Ključne riječi
Gas Classification; Nanostructured Semiconductor Gas Sensors; Volatile Organic Compounds; Extreme Learning Machine
Hrčak ID:
157562
URI
Datum izdavanja:
1.1.2015.
Posjeta: 686 *