A novel electronic nose classification prediction method based on TETCN

Abstract

An efficient algorithm model is proposed to achieve good performance for gas detection based on electronic nose (E-nose) system. Transformer Encoder (TE) has been widely used in natural language processing and shown excellent performance in handling sequence data, which could help the model to learn the contextual information of E-nose data. Temporal Convolutional Network (TCN) is a novel convolutional neural network structure with a receptive field variable in length and the ability to capture long-term dependence, which is suitable for processing time series data. This work proposes a gas classification method based on TETCN which is composed of TE and TCN for processing the data of E-nose. Since hyperparameters have a significant impact on model performance, the Bayesian parameter optimization algorithm is used for TETCN. The data is weighted by TE, and then TCN can easily extract important features, resulting in a satisfactory classification accuracy of 99.8%. The experimental results indicate TETCN has better performance than the conventional methods such as convolutional neural network (CNN), long-short term memory (LSTM), and gated recurrent unit (GRU). Furthermore, network ablation has been implemented to demonstrate the necessity of combining TE and TCN. Finally, the feasibility of rapid detection of the proposed algorithm is discussed.