An intelligent aerator algorithm inspired-by deep learning

Hongjie Deng; Lingxi Peng; Jiajing Zhang; Chunming Tang; Haoliang Fang; Haohuai Liu; Hongjie Deng; Lingxi Peng; Jiajing Zhang; Chunming Tang; Haoliang Fang; Haohuai Liu

doi:10.3934/mbe.2019148

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 4: 2990-3002. doi: 10.3934/mbe.2019148

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Research article Special Issues

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

Received: 06 January 2019 Accepted: 21 March 2019 Published: 10 April 2019

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
- 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[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2990-3002. doi: 10.3934/mbe.2019148

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Abstract

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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