期刊
SENSORS AND ACTUATORS A-PHYSICAL
卷 189, 期 -, 页码 161-167出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.sna.2012.10.023
关键词
Electronic nose; Artificial olfactory system; Back-propagation neural network; Chaotic sequence optimization; Particle swarm optimization
资金
- Key Science and Technology Research Program [CSTC2010AB2002, CSTC2009BA2021]
- Chongqing University Postgraduates' Science and Innovation Fund [CDJXS12160005]
- New Academic Researcher Award for Doctoral Candidates
- Ministry of Education in China
Electronic nose (E-nose), as an artificial olfactory system, can be used for estimation of gases concentration combined with a pattern recognition module. This paper studies the concentration estimations of indoor contaminants for air quality monitoring in dwellings using chaos based optimization artificial neural network integrated into our self-designed portable E-nose instrument. Back-propagation neural network (BPNN) has been recognized as the common pattern recognition. Considering the local optimal flaw of BPNN, this paper presents a novel chaotic sequence optimization BPNN method for improving the accuracy of E-nose prediction. Further comparison with particle swarm optimization is also employed, and maximum 26.03% and 16.4% prediction error decreased after using chaotic based optimization for formaldehyde and benzene concentration estimation. Experimental results demonstrate the superiority and efficiency of the portable E-nose instrument integrated with artificial neural network optimized by chaotic sequence based optimization algorithms in real-time monitoring of air quality in dwellings. (C) 2012 Elsevier B.V. All rights reserved.
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