Deep neural networks for gas concentration estimation and the effect of hyperparameter optimization on the estimation performance

Many studies propose gas concentration estimators using machine learning algorithms owing to their high performance. Recently, estimation models using deep neural network have been studied due to their higher performance than conventional machine learning algorithms. The performance of deep neural network can be increased by hyperparameter optimization. In this paper, we propose two deep neural networks for gas concentration estimation and analyze how hyperparameter optimization affects the performance of the proposed deep neural networks. We optimize the hyperparameters of the proposed neural networks and compare the performance with conventional machine learning models. We train the proposed neural networks and evaluate the performance of the models with an open dataset. We confirm that the optimized neural network models show the high performance in gas concentration estimation, and that models using unoptimized parameters may show worse performance than conventional machine learning model.