Study of A Signal Classification Method in Electronic Noses Based on Suprathreshold Stochastic Resonance
The research on stochastic resonance (SR) in threshold systems has received much attention recently, for multithreshold networks, SR is also observed in snprathreshold system. Generally suprathreshold SR (SSR) has been shown to exist by the mutual information and input-output cross-correlation. In this project, a novel method ofmaximum cross-correlation coefficient based on SSR was proposed to identify five gases gathered by the electronic nose. In the experiment, six carbon nanotubes gas sensors were chosen to compose a sensor array of the electronic nose, which were all sensitive to formaldehyde, benzene, toluene, xylene and ammonia. The data gathered from the sensor array were passed through the SSR system, which was quantified by the cross-correlation coefficient. Form the SSR curves, maximum cross-correlation coefficient of different gas classes was found to be completely different, and the maximum crosscorrelation coefficient was a constant for each gas. So it can be used to accurately represent the different classes of gases. Compared with the classified results of the BP(back propagation) network, the method of maximum cross-correlation coefficient based on SSR has high accuracy in identifying five kinds of gases. So the method of maximum cross-correlation coefficient can he used as a new signal classificaiton method.
suprathreshold stochastic resonance electronic noses carbon nanotubes gas sensor cross-correlation coefficient
Wu Lili Yuan Chao Lin Aiying Zheng Baozhou Guo Miao
College of Science, Henan agricultural University, Zhengzhou, 450002, China Institute of Biomedical Engineering and Instrument, Hangzhou Dianzi University, Hangzhou 210029, Chi
国际会议
长沙
英文
2785-2788
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）