A Novel Regression Prediction Method for Electronic Nose Based on Broad Learning System

When the electronic nose (E-nose) is used to predict the concentration of mixed gas, the traditional regression prediction algorithm may lead to unsatisfactory prediction results and long training time. In order to improve the accuracy of regression prediction and reduce the training time, we proposed a regression prediction algorithm based on broad learning system (BLS) to predict the concentration of mixed gas. To further improve the accuracy of model predictions, we optimize the various parameters existing in the model to improve the performance of the model. Then, we change the initial random mapping weight assignment method of the model to further improve the data processing ability of the model, and the improved model is called GBLS. In the data processing experiment, we use the mixed gas of methane and ethylene as the test gas to test the GBLS model proposed in this article. We have compared GBLS with other existing methods including back propagation neural networks (BPNN), least squares support vector machines (LSSVM), extremely learning machine (ELM), linear regression (LR). Experimental results show that the proposed GBLS outperforms the other methods.