Comparison of Algorithms for an Electronic Nose in Identifying Liquors
When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, the different brands of distilled spirits were identified using the pattern recognition algorithms (principal component analysis and the artificial neural network). The recognition rates of different algorithms were compared. The recognition rate of the Back Propagation Neural Network (BPNN) is the highest. Owing to the slow convergence speed of the BPNN, it tends easily to get into a local minimum. A chaotic BPNN was tried in order to overcome the disadvantage of the BPNN. The convergence speed of the chaotic BPNN is 75.5 times faster than that of the BPNN.
Keywordselectronic nose liquor algorithm principal component analysis
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