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论文类型：期刊论文

第一作者：He, Aixiang

通讯作者：Wei, GF (reprint author), Shandong Technol & Business Univ, Sch Informat & Elect Engn, Yantai 264005, Peoples R China.

合写作者：Wei, Guangfen,Yu, Jun,Tang, Zhenan,Lin, Zhonghai,Wang, Pingjian

发表时间：2017-12-01

发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录刊物：Scopus、SCIE、EI

卷号：64

期号：12

页面范围：9709-9715

ISSN号：0278-0046

关键字：Dictionary learning (DL); electronic nose; gas identification; gas sensor array

摘要：Discriminative dictionary learning has been successfully applied in pattern recognition field. In most of dictionary learning methods, l0-norm or l1-norm is used to regularize the sparse representation coefficients, which makes the computing time consuming. In this paper, we present a novel dictionary learning method to improve the gas identification performance of the electronic nose. It has significantly less complexity but leads to very competitive classification results. An analysis dictionary is trained to generate discriminative code by a simple linear projection, while a synthesis dictionary is trained to obtain discriminative reconstruction. Moreover, class label information is utilized to promote the discriminative power of the coding coefficients. The analysis dictionary and synthesis dictionary are trained jointly by an iterative method, which makes the learned projection dictionaries better fit with each other so that the more effective gas identification can be obtained. The proposed algorithm is evaluated on the analysis of different concentration of carbon monoxide, methane, hydrogen, benzene, formaldehyde, ethylene, propane, and ethanol. Experimental results show that the proposed method is not only effective in the signal analysis, but also useful and applicable to the performance enhancement of the current electronic noses.