期刊
SENSORS AND ACTUATORS B-CHEMICAL
卷 362, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2022.131812
关键词
Gas sensor; Temperature and humidity compensation; Improved deep BP neural network; Self-learning; Self-adaptability; Recognition accuracy
资金
- National Natural Science Foundation of China [U19A2070]
- Innovation Project of Chinese Academy of Agricultural Sciences [CAAS-ASTIP-2016-AII-02]
This study presents an improved deep Back Propagation neural network model to compensate for the impact of environmental factors on gas sensors, achieving significant performance improvement and training time reduction.
The accuracy and reliability of gas sensor are directly by temperature and humidity. In this study, an improved deep Back Propagation (BP) neural network was designed to lower the impact of environmental factors on NO2 gas sensor based on PbS nanoparticles sensitive film. The contradiction between high performance and low time complexity was usually faced by current compensation methods of gas sensor. Moreover, poor self-learning ability always resulted in more recognition errors. To solve the above problems, a 14-layer deep BP neural network model was constructed after hyperparameter searching. Stochastic Gradient Descent (SGD) algorithm with Mini-batch algorithm was adopted to well balance the model performance and the training time complexity, resulting in 76.68% performance improvement and nearly 6 times training time reduction after 1000 epochs, respectively. Softplus activation function was combined with Adam optimizer to further improve the model performance with a good recognition accuracy (1.37% relative error, corresponding to 0.0087 Mean Square Error (MSE)). The self-learning and self-adaptability of the improved deep BP neural network made it an excellent compensation method for the gas sensor applied in complex environments.
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