The drift is the inherent behavior of gas sensor,so it is more generality to reveal drift phenomena with no-load data.In order to remove the drift effectively,under the no-load condition,a drift removal method based on wavelet packet decomposition was proposed.Firstly,wavelet packet decomposition was employed to decompose the no-load data of the E-nose,and the approximation coefficient set of wavelet packet decomposition could be obtained.After the discrete analysis of the approximation coefficient set was carried out,a threshold function based on no-load data of the E-nose was constructed.And then the drift threshold function based on the sample data (loaded data) was obtained by extending the threshold function based on no-load data;furthermore,a drift elimination method for sample data was given.To test the effectiveness and practicability of the above method,it was applied to identify four kinds of white spirit samples by using the E-nose.The E-nose data of the four kinds of samples were divided into training set and test set according to the test time sequence,the identification results of linear Fisher discriminant analysis (FDA) indicated that the identification correction rates of training set and test set were all improved after their data were processed by the above drift removal method,and the minimum improvement was 23.65％,which showed that the method can effectively enhance the detection ability of the E-nose.At the same time,in order to further test the performance of the drift removal method,the nonlinear BP neural network was used to identify the four kinds of samples,and its identification results displayed that after treatment with the method,the identification correction rate of the training set was from 65.5％ up to 100％,and the identification correction rate of the test set was also up to 97.5％.This not only showed that the identification of the four kinds of white spirit samples was a complicated nonlinear classification problem,but also showed that the proposed drift removal method was very effective.In addition,the drift removal method was proposed according to the no load data of the E-nose,thus it was considered to be general.%为了有效去除电子鼻漂移,提出了一种基于空载条件小波包分解的漂移去除方法.对电子鼻空载数据进行小波包分解,获得小波包分解的逼近系数集;在对其进行离散度分析之后,构建了空载条件下的一种阈值函数.在此阈值函数基础上,扩展成为样本(有载)条件下的去漂移阈值函数,进而发展成有载样本的漂移剔除方法.为了检验该方法的有效性及实用性,将其应用于4种白酒的鉴别中.对4种白酒电子鼻数据按测试时间顺序生成训练集和测试集,线性的Fisher判别分析结果表明,训练集、测试集数据处理后的鉴别正确率均得到了提高,最低提高值为23.65％.表明此方法能够提升电子鼻的检测能力.同时,为了进一步检验该漂移去除方法的性能,采用非线性的BP神经网络进行鉴别分析,结果显示:训练集的鉴别正确率从处理前的65.5％提高到处理后的100％,处理后的测试集鉴别正确率也达到了97.5％.这不仅说明了4种白酒的鉴别属较复杂的非线性分类问题,还充分说明了该漂移去除方法的有效性.
展开▼
