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Indexed by:Journal Papers

Date of Publication:2015-12-22

Journal:NANOMATERIALS AND NANOTECHNOLOGY

Included Journals:SCIE

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.