An optimized deep convolutional neural network for dendrobium classification based on electronic nose

Abstract

This paper introduces an optimized deep convolutional neural network (DCNN) using special banded 1D kernels at the convolutional and the pooling layers adapted for electronic nose (E-nose) data. It is used to classify multiple types of Chinese herbal medicine. The optimized DCNN network is composed of 5 special convolutional layers with 1D convolutional kernels, 2 special pooling layers with 1D size, 1 fully connected layer and 1 Softmax layer. Results show that the optimized DCNN achieves the best accuracy of 87.56%, outperforming the 81.67% from the second-best classifier DCNN. The optimized DCNN extracts features from E-nose data faster and better than common DCNN. This paper also proposes an insight of applying DCNN to small-scale and E-nose data.