Zhao Wei, Meng Qing-Hao, Zeng Ming, Qi Pei-Feng
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2017 Dec 8;17(12):2855. doi: 10.3390/s17122855.
This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods for e-noses. However, these steps are complicated and empirical because there is no uniform rule for choosing appropriate methods from many different options. The main advantage of SSAE is that it can automatically learn features from the original sensor data without the steps of preprocessing and feature extraction; which can greatly simplify data processing procedures for e-noses. To identify different brands of Chinese liquors; an SSAE based multi-layer back propagation neural network (BPNN) is constructed. Seven kinds of strong-flavor Chinese liquors were selected for a self-designed e-nose to test the performance of the proposed method. Experimental results show that the proposed method outperforms the traditional methods.
本文提出了一种基于堆叠稀疏自编码器（SSAE）的深度学习方法，用于电子鼻（e-nose）系统对不同品牌的中国白酒进行分类。众所周知，预处理、特征提取（生成和约简）是电子鼻传统数据处理方法中的必要步骤。然而，这些步骤复杂且凭经验，因为从众多不同选项中选择合适方法没有统一规则。SSAE的主要优点是它可以从原始传感器数据中自动学习特征，而无需预处理和特征提取步骤，这可以大大简化电子鼻的数据处理程序。为了识别不同品牌的中国白酒，构建了基于SSAE的多层反向传播神经网络（BPNN）。选择了七种浓香型中国白酒用于自行设计的电子鼻，以测试所提方法的性能。实验结果表明，所提方法优于传统方法。
