Indexed by:
Journal Papers
First Author:
Zhao, Lin
Correspondence Author:
Wang, J (reprint author), Dalian Univ Technol, Sch Elect Sci & Technol, Dalian, Peoples R China.
Co-author:
Wang, Jing,Li, Xiaogan
Date of Publication:
2015-12-22
Journal:
NANOMATERIALS AND NANOTECHNOLOGY
Included Journals:
SCIE
Document Type:
J
Volume:
5
ISSN No.:
1847-9804
Key Words:
Gas Classification; Nanostructured Semiconductor Gas Sensors; Volatile
Organic Compounds; Extreme Learning Machine
Abstract:
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.
Translation or Not:
no
