Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose

• Published in: Sensors and actuators. A. Physical. 2013, Vol.189, p.161-167
• Main Authors: Zhang, Lei, Tian, Fengchun, Liu, Shouqiong, Guo, Jielian, Hu, Bo, Ye, Qi, Dang, Lijun, Peng, Xiongwei, Kadri, Chaibou, Feng, Jingwei
• Format: Article
• Language: English
• Subjects: Artificial olfactory system; Back-propagation neural network; Chaotic sequence optimization; Electronic nose; Particle swarm optimization
• ISSN: 0924-4247; 1873-3069
• DOI: 10.1016/j.sna.2012.10.023

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.