期刊
IFAC PAPERSONLINE
卷 51, 期 17, 页码 831-836出版社
ELSEVIER
DOI: 10.1016/j.ifacol.2018.08.091
关键词
water quality prediction; dissolved oxygen; sparse auto-encoder; long-short-term memory network
资金
- International Science & Technology Cooperation Program of China [2015DFA00530]
- Key Research and Development Research Project of Shandong Province [2016CYJS03A02]
- National Nature Science Foundation of China Project [61471133, 61472172]
In order to improve the prediction accuracy of dissolved oxygen in aquaculture, a hybrid model based on sparse auto-encoder (SAE) and long-short-term memory network (LSTM) is proposed in this paper. The hidden layer data pre-trained by SAE contains deep latent features of water quality, and then input it into the LSTM to enhance the prediction accuracy. Experimental results show that SAE-LSTM surpasses LSTM through reducing MSE respectively by 23.3%, 53.6%, and 39.2% in the prediction steps of 3, 6, and 12 hours, and surpasses SAE-BPNN by 87.7%, 91.9%, and 90.0%, proving that our hybrid model is more accurate. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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