Electronic nose (E-nose) technology, inspired by the biological olfactory system, has exhibited substantial promise across diverse applications due to its effective odor detection and identification capabilities. This paper presents a novel approach that leverages a bioinspired olfactory bulb model for processing data within E-nose systems. In this study, the complex neural circuits within the olfactory bulb were simulated using a bioinspired model. This method directly harnesses the inherent feature extraction capabilities of the olfactory bulb model, eliminating redundant steps and thereby enhancing classification accuracy. To evaluate the performance of this approach, ten types of toxic gases were classified using Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Linear Discriminant Analysis (LDA) classifiers. The proposed bioinspired olfactory bulb model was compared to traditional data preprocessing methods. Experimental results clearly demonstrate that the bioinspired olfactory bulb model significantly improves classification accuracy compared to traditional methods.
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%.
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