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An intelligent aerator algorithm inspired-by deep learning

1 School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China
2 School of Mathematics and information science, Guangzhou University, Guangzhou, 510006, China
3 School of Chemistry, Guangzhou University, Guangzhou 510006, China

Special Issues: Neural Computation and Applications for Sustainable Energy Systems

Aerator is an indispensable tool in aquaculture, and China is one of the largest aquaculture countries in the world. So, the intelligent control of the aerator is of great significance to energy conservation and environmental protection and the prevention of the deterioration of dissolved oxygen. There is no intelligent aerator related work in practice and research. In this paper, we mainly study the intelligent aerator control based on deep learning, and propose a dissolved oxygen prediction algorithm with long and short term memory network, referred as DopLSTM. The prediction results are used to the intelligent control design of the aerator. As a result, it is proved that the intelligent control of the aerator can effectively reduce the power consumption and prevent the deterioration of dissolved oxygen
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Keywords intelligent aerator; long term memory network; deep learning

Citation: Hongjie Deng, Lingxi Peng, Jiajing Zhang, Chunming Tang, Haoliang Fang, Haohuai Liu. An intelligent aerator algorithm inspired-by deep learning. Mathematical Biosciences and Engineering, 2019, 16(4): 2990-3002. doi: 10.3934/mbe.2019148


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  • 2. Zhong Xiao, Jingtong Wang, Chen Yi, Zairong Wang, Liang Wang, The Dissolved Oxygen Sensor Design Based on Ultrasonic Self-Adaption and Self-Cleaning, Complexity, 2020, 2020, 1, 10.1155/2020/3297203

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