A convolutional spiking neural network combined with residual blocks for electronic nose data processing

Abstract

The traditional method of electronic nose (E-nose) data processing has the disadvantages of cumbersome operation steps and low classification accuracy. To address these problems, this paper proposes a convolutional spiking neural network (CSNN) for E-nose data processing that combines residual blocks. The network model consists of spiking-convolutional layers and fully connected pulse layers. The model combines the feature extraction capability of a convolutional neural network (CNN) with the computational efficiency of a spiking neural network (SNN) and the good biointerpretability of spike signal data and makes use of residual blocks to allow the network to learn richer content. In addition, two spike coding methods (response rate coding and response value coding) are designed to encode the data to make great use of the sensor curve features. To test the performance of the proposed network model in the E-nose, the data collected by the self-built E-nose system were used to identify and classify ten toxic gases with a maximum classification rate of 96.39%.