School of Automation Engineering, Northeast Dianli University, Jilin City 132012, China.
Sensors (Basel). 2011;11(5):5005-19. doi: 10.3390/s110505005. Epub 2011 May 4.
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.
鉴于传统竞争神经网络（CNNs）在电子鼻（E-nose）中应用存在类数必须提前确定、学习率取值困难等缺点，提出了一种优化的 CNN 方法。该优化的 CNN 是根据 Davies 和 Bouldin（DB）值的变化，在样本最优类数的基础上建立的，可以增加、划分或删除神经元，从而自动调整神经元的数量。此外，学习率根据每个样本的训练次数的变化而变化。将传统 CNN 和优化的 CNN 应用于电子鼻的 5 种分类醋中。结果表明，优化的网络结构可以动态调整聚类数，从而得到较好的分类效果。
