[1] Zhang X C, Endo M, Sakamoto T, et al. Studies on kuruma shrimp culture in recirculating aquaculture system with artificial ecosystem[J]. Aquaculture, 2018, 484:191-196. [2] Li C, Li Z B, Wu J, et al. A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features[J]. Information Processing in Agriculture, 2017, 1(5):11-20. [3] Guseo R. Diffusion of innovations dynamics, biological growth and catenary function[J]. Physica A:Statistical Mechanics & Its Applications, 2016, 464:1-10. [4] Scully E M. Mixing of dissolved oxygen in Chesapeake Bay driven by the interaction between wind-driven circulation and estuarine bathymetry[J]. Journal of Geophysical Research, 2016, 121(8):5639-5654. [5] 王小艺, 赵晓平, 刘载文, 等. 基于灰色理论的湖库水体富营养化预测方法研究[J]. 计算机仿真, 2011, 28(1):17-19. Wang X Y, Zhao X P, Liu Z W, et al. Research on lake eutrophication forecasting methods based on grey theory[J]. Computer Simulation, 2011, 28(1):17-19. (in Chinese) [6] Xiao Z, Peng L X, Chen Y, et al. The dissolved oxygen prediction method based on neural network[J]. Complexity, 2017:1-6. [7] Liu S Y, Yan M X. Prediction of dissolved oxygen content in aquaculture of hyriopsis cumingii using Elman neural network[C]//2012 International Conference on Computer and Computing Technologies in Agriculture, 2012:508-518. [8] Chen Y Y, Xu J, Yu H H, et al. Three-dimensional short-term prediction model of dissolved oxygen content based on PSO-BPANN algorithm coupled with Kriging interpolation[J]. Mathematical Problems in Engineering, 2016:6564202-6564210. [9] Khotimah W N. Aquaculture water quality prediction using smooth SVM[J]. IPTEK Journal of Proceedings, 2014(1):342-345. [10] Liu S Y, Tai H J, Ding Q S, et al. A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction[J]. Mathematical & Computer Modelling, 2013, 58(3/4):458-465. [11] Huan J, Cao W J, Qin Y L. Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework[J]. Computers and Electronics in Agriculture, 2018, 150:257-265. [12] Khadr M, Elshemy M. Data-driven modeling for water quality prediction case study:the drains system associated with Manzala Lake, Egypt[J]. Ain Shams Engineering Journal, 2017, 8(4):549-557. [13] Sahoo M M, Patra K C, Khatua K K. Inference of water quality index using ANFIA and PCA[J]. Aquatic Procedia, 2015(4):1099-1106. [14] Xu L Q, Liu S Y. Study of short-term water quality prediction model based on wavelet neural network[J]. Mathematical & Computer Modelling, 2013, 58(3/4):807-813. [15] Shi P, Li G H, Yuan Y M, et al. Prediction of dissolved oxygen content in aquaculture using clustering-based Softplus extreme learning machine[J]. Computers and Electronics in Agriculture, 2019(157):329-338. [16] Ta X X, Wei Y G. Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network[J]. Computers and Electronics in Agriculture, 2018(145):302-310. [17] Ta X X, An D, Wei Y G. Dissolved oxygen prediction method for recirculating aquaculture system, based on a timing attenuation matrix and a convolutional neural network[J]. Aquaculture, 2019(503):26-33. [18] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]//European Conference on Computer Vision, 2014:818-833. [19] Jafrasteh B, Fathianpour N. A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation[J]. Neurocomputing, 2017, 235:217-227. [20] Schmidhuber J. Deep learning in neural networks:an overview[J]. Neural Networks, 2015, 61:85-117. [21] Monir E C, Hicham O, Mohamed L. Scientific paper classification using convolutional neural networks[C]//International Conference Big Data and Internet Things, Tangier, Morocco, 2019:1-6. [22] Yu R, Gao Y, Duan X, et al. QRS detection and measurement method of ECG paper based on convolutional neural networks[C]//201840th Annual International Conference of the IEEE Engineering in Medicine and Biology Society:4636-4639. [23] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 8(9):1735-1780. [24] Gers F A, Schmidhuber J. Recurrent nets that time and count[C]//Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000:189-194. [25] Greff K, Srivastava R K, Koutník J, et al. LSTM:a search space odyssey[J]. IEEE Transactions on Neural Networks & Learning Systems, 2015, 28(10):2222-2232. [26] Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. Eprint Arxiv, 2014. 2014[2020-08-15]. https://arxiv.org/abs/1412.3555. [27] Shi X J, Chen Z R, Wang H, et al. Convolutional LSTM network:a machine learning approach for precipitation nowcasting:NIPS'15[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, Cambridge, 2015:802-810. |