SnapE-ResNet: A novel electronic nose classification algorithm for gas data collected by open sampling systems

Increasing the depth of the neural network and implementing the ensemble of neural networks are two main methods to improve the accuracy of gas recognition algorithms. However, the issues of optimization difficulties or excessive resource consumption may occur when training deep neural networks or integrating multiple neural networks. In this study, an algorithm of snapshot ensemble is combined with Residual network (ResNet) to form a SnapE-ResNet which could simplify the training of the network due to the residual structure of ResNet, while realizes the ensemble of multiple ResNet models without additional time consumption and helps the network escape from local minimum using cyclic cosine annealing algorithm. The effectiveness of the proposed SnapE-ResNet is verified through classification experiments on the public dataset. Furthermore, the long-term drift and short-term drift are reduced by applying a zero-offset baseline compensation algorithm and a gaussian noise that added to the baseline signal. The experimental result indicating a high gas classification accuracy of 99.8 % from SnapE-ResNet is obtained, outperforming the comparative models. In addition, the necessity for snapshot ensemble is validated by network ablation experiments. This study could offer a reference for gas classification tasks in the gas detection fields. [Display omitted] •The long-term drift and short-term drift are reduced by two compensation algorithms.•The novel network SnapE-ResNet is designed to realize network integration without additional time consumption.•The SnapE-ResNet is compared with multiple models, and the necessity of SnapE is verified through network ablation.