Combining PSO-KECA with ELM in an electronic nose for classification of Chinese liquors

We designed an electronic nose to classify different Chinese liquors. Kernel entropy component analysis (KECA) was applied to reduce the dimensionality of data sets. In order to avoid the blindness of parameter setting, particle swarm optimization (PSO) algorithm was employed to optimize parameters in KECA. At last, we adopted extreme learning machine (ELM) as a classifier to classify eight kinds of strong-flavor Chinese liquors. The results indicate that ELM has a better performance for classification of Chinese liquors with different brands than back propagation neural network (BPNN). The highest classification rate by ELM is 97.5%.

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