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

Authors
Journal
Sensors and Actuators A Physical
0924-4247
Publisher
Elsevier
Publication Date
Volume
189
Identifiers
DOI: 10.1016/j.sna.2012.10.023
Keywords
  • Electronic Nose
  • Artificial Olfactory System
  • Back-Propagation Neural Network
  • Chaotic Sequence Optimization
  • Particle Swarm Optimization
Disciplines
  • Computer Science
  • Design

Abstract

Abstract 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.

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