A Novel Gas Recognition and Concentration Detection Algorithm for Artificial Olfaction

A novel gas recognition and concentration detection algorithm consisting of a dynamic wavelet convolutional neural network (DWCNN) and a many-to-many long short-term memory-recurrent neural network (LSTM-RNN), respectively, is proposed as a replacement for the traditional data processing algorithm in artificial olfaction. The proposed DWCNN gas recognition algorithm does not require gas signal preprocessing, and it directly converts the raw time-domain gas signal data to 64 ∗ 64 2-D gray images as the input layer of a convolutional neural network (CNN). The experiments show that the recognition accuracy of CO, H2, and the gas mixture of CO and H2 is nearly 100%, and the many-to-many LSTM-RNN algorithm requires only a few labeled data from the steady-state values of the gas sensor array signals. In addition, comparisons with other neural network multilayer perceptrons (MLPs), gated recurrent unit (GRU) algorithms, and conventional algorithms, such as the Bayesian ridge, support vector machines (SVMs), decision tree, k-nearest neighbor (KNN), random forest, AdaBoost, gradient-boosting decision tree (GBDT), and bagging, revealed that the algorithm can obtain a higher concentration detection accuracy, which was evaluated using two different kernel functions for the kernel principal component analysis (KPCA) dimensionality reduction: polynomial and rbf. The experimental results demonstrated that the proposed many-to-many LSTM gas concentration detection model outperformed the abovementioned algorithms and can more accurately estimate the concentration of different gases while using less labeled data.