Bionic Information Processing Technique For Electronic Nose Based On An Olfactory Neural Network

Electronic nose is a kind of bionic instrument to mimic the mechanism of biological olfactory systems, which generally consists of an array of cross-sensitive chemsensors and an appropriate pattern recognition algorithm, capable of automatically detecting and discriminating odors. Due to the advantages of simply, objective and low-cost compared to human panels and conventional analytical instruments, electronic noses have been widely used in many areas. Moreover, resent basic research findings in biological olfaction combined with computational neuroscience promote its development both in methodology and application. This thesis studies an olfactory neural network entitled Kâ…¢and introduces it to electronic nose community as a bionic information processing technique.Firstly, the numeral analysis of the Kâ…¢network is carried out. The simulation results show that the properties of its dynamics are much similar with that of EEG signals observed in biological experiments.The topological properties of the Kâ…¢network is also studied using the small-world network theory and wavelet decomposition method. The results show the Kâ…¢network with some small-world network characteristics, similar to which in complex biological systems.Then, the application of the Kâ…¢network to some theoretical problems in artificial olfaction is investigated, such as sensor drift counteraction and concentration influence elimination. Experimental data of six volatile organic compounds obtained through a home-made electronic nose set-up are processed by the Kâ…¢network. The results show that the Kâ…¢has good performances in classification of samples with sensor drift or concentration variation. In this thesis, a spatio-temporal pattern processing conception is proposed, which seamless integrating the Kâ…¢network into electronic nose system, making the electronic nose work more similar as a biological olfaction does.Furthermore, the pattern recognition method based on the Kâ…¢network is improved, through combining it with support vector machine to construct different series classifier and ensemble classifier. And their performances are explored in the case of classifying four kinds of teas and six different bands of fresh milk and constructed milk, respectively.