[1] |
余成洲, 李勇, 白云 . 基于集合经验模态分解和支持向量机的溶解氧预测[J]. 环境监测管理与技术, 2018,30(03):27-31.
|
|
Yu C, Li Y, Bai Y . DO prediction based on ensemble empirical mode decomposition and support vector machine[J]. The Administration and Technique of Environmental Monitoring, 2018,30(03):27-31.
|
[2] |
陈彦, 殷建军, 项祖丰 , 等. 基于时间序列模型的海洋溶解氧分析与预测[J]. 轻工机械, 2012,30(03):83-87.
|
|
Chen Y, Yin J, Xiang Z , et al. Marine dissolved oxygen analysis and prediction based on the time series model[J]. Light Industry Machinery, 2012,30(03):83-87.
|
[3] |
Chen W, Liu W . Artificial neural network modeling of dissolved oxygen in reservoir[J]. Environmental monitoring and assessment, 2014,186(2):1203-1217.
doi: 10.1007/s10661-013-3450-6
|
[4] |
Olyaie E, Abyaneh H Z, Mehr A D . A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River[J]. Geoscience Frontiers, 2017,8(3):517-527.
doi: 10.1016/j.gsf.2016.04.007
|
[5] |
袁红春, 潘金晶 . 改进递归最小二乘RBF神经网络溶解氧预测[J]. 传感器与微系统, 2016,35(10):20-23.
|
|
Yuan H, Pan J . Dissolved oxygen prediction based on improved recursive least square RBF neural network[J]. Transducer and Microsystem Technologies, 2016,35(10):20-23.
|
[6] |
宦娟, 刘星桥 . 基于K-means聚类和ELM神经网络的养殖水质溶解氧预测[J]. 农业工程学报, 2016,32(17):174-181.
|
|
Huan J, Liu X . Dissolved oxygen prediction in water based on K-means clustering and ELM neural network for aquaculture[J]. Transactions of the CSAE, 2016,32(17):174-181.
|
[7] |
施珮, 袁永明, 张红燕 , 等. GRNN和Elman神经网络在水体溶解氧预测中的应用[J]. 江苏农业科学, 2017,45(23):217-221.
|
[8] |
吴静, 李振波, 朱玲 , 等. 融合ARIMA模型和GAWNN的溶解氧含量预测方法[J]. 农业机械学报, 2017,48(S1):205-210, 204.
|
|
Wu J, Li Z, Zhu L , et al. Hybrid model of ARIMA model and GAWNN for dissolved oxygen content prediction[J]. Transactions of the CSAM, 2017,48(S1):205-210, 204.
|
[9] |
魏小敏, 张宝峰, 朱均超 , 等. 基于PSO优化RBF神经网络的溶解氧预测算法研究[J]. 自动化与仪表, 2018,33(05):57-60.
|
|
Wei X, Zhang B, Zhu J , et al. Remote monitoring system of fishery breeding based on Internet of Things[J]. Automation & Instrumentation, 2018,33(05):57-60.
|
[10] |
Zhu N, Liu X, Liu Z , et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities[J]. International Journal of Agricultural and Biological Engineering, 2018,11(4):32-44.
|
[11] |
李志刚, 纪月, 任雄朝 . 基于LSTM与ARIMA组合模型的高炉煤气产生量预测[J]. 铸造技术, 2018,39(11):2456-2460.
|
|
Li Z, Ji Y, Ren X . Prediction of blast furnace gas output based on combined model of LSTM and ARIMA[J]. Foundry Technology, 2018,39(11):2456-2460.
|
[12] |
范竣翔, 李琦, 朱亚杰 , 等. 基于RNN的空气污染时空预报模型研究[J]. 测绘科学, 2017,42(07):76-83, 120.
|
|
Fan J, Li Q, Zhu Y , et al. Aspatio-temporal prediction framework for air pollution based on deep RNN[J]. Science of Surveying and Mapping, 2017,42(07):76-83, 120.
|
[13] |
Ma X, Tao Z, Wang Y , et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J]. Transportation Research Part C: Emerging Technologies, 2015,54:187-197.
doi: 10.1016/j.trc.2015.03.014
|
[14] |
杨祎玥, 伏潜, 万定生 . 基于深度循环神经网络的时间序列预测模型[J]. 计算机技术与发展, 2017,27(03):35-38, 43.
|
|
Yan Y, Fu Q, Wan D . A Prediction model for time series based on deep recurrent neural network[J]. Computer Technology and Development, 2017,27(03):35-38, 43.
|
[15] |
Akita R, Yoshihara A, Matsubara T, et al. Deep learning for stock prediction using numerical and textual information [C]. IEEE/ACIS International Conference on Computer & Information Science. IEEE, 2016.
|
[16] |
石磊, 张鑫倩, 陶永才 , 等. 结合自注意力机制和Tree-LSTM的情感分析模型[J]. 小型微型计算机系统, 2019,40(07):1486-1490.
|
|
Shi L, Zhang X, Tao Y , et al. Sentiment analysis model with the combination of self-attention and Tree-LSTM[J]. Journal of Chinese Computer Systems, 2019,40(7):1486-1490.
|
[17] |
谢明磊 . 基于LSTM网络的住宅负荷短期预测[J]. 广东电力, 2019,32(06):108-114.
|
[18] |
Najah A, El-Shafie A, Karim O A , et al. An application of different artificial intelligences techniques for water quality prediction[J]. International Journal of Physical Sciences, 2011,6(22):5298-5308.
|
[19] |
Wen X, Fang J, Diao M , et al. Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China[J]. Environmental Monitoring and Assessment, 2013,185(5):4361-4371.
doi: 10.1007/s10661-012-2874-8
|
[20] |
Rankovic V, Radulovic J, Radojevic I , et al. Neural network modeling of dissolved oxygen in the Gruza reservoir[J]. Serbia. Ecological Modelling, 2010,221(8):1239-1244.
|
[21] |
陈英义, 程倩倩, 方晓敏 , 等. 主成分分析和长短时记忆神经网络预测水产养殖水体溶解氧[J]. 农业工程学报, 2018,34(17):183-191.
|
|
Chen Y, Cheng Q, Fang X , et al. Principal component analysis and long short-term memory neural network for predicting dissolved oxygen in water for aquaculture[J]. Transactions of the CSAE, 2018,34(17):183-191.
|
[22] |
Hochreiter S, Schmidhuber J . Long short-term memory[J]. Neural Computation, 1997,9(8):1735-1780.
doi: 10.1162/neco.1997.9.8.1735
|
[23] |
Cohen P, West S G, Aiken L S. Applied multiple regression/correlation analysis for the behavioral sciences[M]. New York: Psychology Press, 2014.
|
[24] |
Abdi H, Williams L J . Principal component analysis[J]. Wiley Interdisciplinary Reviews Computational Statistics, 2010,2(4):433-459.
doi: 10.1002/wics.101
|
[25] |
Willmott C J, Matsuura K . Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance[J]. Climate Research, 2005,30(1):79-82.
doi: 10.3354/cr030079
|