Prediction of CO concentration in different conditions based on Gaussian-TCN

Accurate prediction of gas concentrations can help people to know if there is hazardous gas in the environment, thus protecting people's health. The electronic nose (E-nose) performance in predicting gas concentrations mostly depends on its algorithm. In recent years, recurrent neural networks (RNNs) have demonstrated their suitability for processing time series data. Typical models are long short-term memory (LSTM) neural networks and gated recursive units (GRUs). However, traditional recurrent neural networks use a gate structure to drop some information randomly, which can cause important features to be discarded. The temporal convolutional neural network (TCN) based on convolutional structures is more suitable for time series prediction. To enhance the accurate model forecasts, we first optimized the critical parameters in the model. Then, we adapted the residual structure of TCN and replaced the activation function of TCN with Gaussian error linear units (GELUs), And the modified model is named Gaussian-TCN. Finally, the experimental results show that the proposed Gaussian-TCN outperforms traditional recurrent networks regarding prediction accuracy. • An electronic nose with sensors is used to predict CO concentrations under different conditions. • Large-scale measurements with more than ten million data points are used to train a deep learning model for the electronic nose. • The paper proposes a new deep learning model called Gaussian-TCN, which is based on the temporal convolutional neural network and Gaussian error linear units. • The proposed Gaussian-TCN achieves significant predictive performance and outperforms TCN, GRU, and LSTM.