Journal
IEEE SENSORS JOURNAL
Volume 16, Issue 22, Pages 8010-8017Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2016.2606163
Keywords
Feature reduction; kernel entropy component analysis; linear discriminant analysis; extreme learning machine; Chinese liquors; e-nose
Funding
- National Natural Science Foundation of China [61573253, 61271321, 61401303, 51578189]
- Ph.D. Programs Foundation of the Ministry of Education of China [20120032110068]
- Tianjin Science and Technology Commission [14ZCZDSF00025, 14RCHZGX00862]
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We proposed a hybrid algorithm by combining kernel entropy component analysis (KECA) with linear discriminant analysis (LDA), namely, KECA-LDA for feature reduction in electronic-nose systems. It combined the advantages of KECA and LDA. Then, the data extracted by KECA-LDA were inputted to extreme learning machine (ELM) for classification. In order to examine the performance of the proposed method, eight types of strong-flavor Chinese liquors were tested using an electronic nose (e-nose) system designed by ourselves, and the results after cross validation showed that features extracted by KECA-LDA were more beneficial to classification than KECA, and the performance of ELM was better than that of back-propagation neural network. The highest classification rate by KECA-LDA-ELM was 100%. In conclusion, an e-nose combined with KECA-LDA and ELM is a feasible method to classify Chinese liquors.
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