Abstract
In view of the fact that there are disadvantages in that the class number must be determined in advance, the value of learning rates are hard to fix, etc., when using traditional competitive neural networks (CNNs) in electronic noses (E-noses), an optimized CNN method was presented. The optimized CNN was established on the basis of the optimum class number of samples according to the changes of the Davies and Bouldin (DB) value and it could increase, divide, or delete neurons in order to adjust the number of neurons automatically. Moreover, the learning rate changes according to the variety of training times of each sample. The traditional CNN and the optimized CNN were applied to five kinds of sorted vinegars with an E-nose. The results showed that optimized network structures could adjust the number of clusters dynamically and resulted in good classifications.
Highlights
The volatile odor of substances such as alcohol, tobacco, tea, food, etc. is closely linked to their quality
This paper presents an open competitive neural networks (CNNs) structure which in terms of the Davies and Bouldin (DB) value [29] determines the number of output neurons, the best number of clusters
In 40 seconds, the sensor signal has an obvious ascendant tendency; the data was collected and transferred to the computer through the data acquisition card. This data collection was maintained for 2 minutes, the switch of the fan was controlled according to the data from the integrated temperature and humidity sensor lest any great change in temperature and humidity affect the results of the experiments
Summary
The volatile odor of substances such as alcohol, tobacco, tea, food, etc. is closely linked to their quality. (4) The selection rule of the learning rate has a conflict between the convergence speed and the stability of the system These shortcomings restrict the application of the algorithm in electronic noses. When evaluating the grade of tobacco, spices, and food freshness with electronic noses, the classification number of samples is not predictable and sometimes a new sample is not the same as the original samples stored in the network. Applied to the classification of five kinds of vinegar with an EN, and the results showed that the network had a good dynamic classification; the network structure was stable, and quickly converged
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