A Novel Convolutional Recurrent Neural Network Based Algorithm for Fast Gas Recognition in Electronic Nose System

摘要

In this paper, we present a novel fast gas recognition algorithm based on convolutional recurrent neural network. Rectangular kernels and long short-term memory (LSTM) are adopted to calculate the gas-label distribution from relationship between sensors and relationship among time as well. Compared with the widely adopted algorithms, such as support vector machine (SVM), K nearest neighbor (KNN), random forest (RF), the proposed implementation features an ultra-short detection time of 4 seconds. Moreover, according to our extensive experimental results, a high accuracy of 98.28% is achieved, which greatly outperforms the abovementioned algorithms.