| 引用本文: | 陈中林,杨翠丽,乔俊飞.基于TG–LSTM神经网络的非完整时间序列预测[J].控制理论与应用,2022,39(5):867~878.[点击复制] |
| CHEN Zhong-lin,YANG Cui-li,QIAO Jun-fei.The prediction of incomplete time series via TG–LSTM neural network[J].Control Theory and Technology,2022,39(5):867~878.[点击复制] |
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| 基于TG–LSTM神经网络的非完整时间序列预测 |
| The prediction of incomplete time series via TG–LSTM neural network |
| 摘要点击 1840 全文点击 514 投稿时间：2021-07-30 修订日期：2022-01-23 |
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| DOI编号 10.7641/CTA.2021.10693 |
| 2022,39(5):867-878 |
| 中文关键词 数据缺失 非完整时间序列 长短期记忆神经网络 前向传播算法 学习算法 |
| 英文关键词 data missing incomplete time series long short-term memory neural network forward propagation algorithm learning algorithm |
| 基金项目 国家自然科学基金项目(61890930 – 5, 62021003, 61973010), 国家重点研发计划项目(2021ZD0112302), 北京市自然科学基金项目(4202006) 资助. |
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| 中文摘要 |
| 针对传统模型对含数据缺失的非完整时间序列预测精度不高的问题, 利用长短期记忆(LSTM)神经网络强
大的时序建模能力, 提出一种带时间门的长短期记忆(TG–LSTM)神经网络. 首先, 提出一种能同时对输入值在线估
计和输出值实时预测的TG–LSTM单元结构; 其次, 基于TG–LSTM结构设计一种网络的前向传播算法, 实现输入填
补和输出预测同步进行; 然后, 建立TG–LSTM神经网络的学习算法来对输入填补和输出预测任务整体训练; 最后,
通过在Mackey-glass基准数据集, 月平均气温数据集和污水处理出水氨氮预测中的实验结果表明: 与传统方法相比,
TG–LSTM神经网络模型能以更高精度对非完整时间序列进行填补和预测. |
| 英文摘要 |
| In order to solve the problem that the poor prediction accuracy of traditional model for the time series with
missing data, the strong time series modeling ability of long short-term memory (LSTM) neural network is used and the
long short-term memory neural network with time gate (TG–LSTM) is proposed. Firstly, the TG–LSTM unit structure is
proposed, which can be used to realize the online estimation of network inputs and real-time prediction outputs, simultaneously.
Secondly, the forward propagation algorithm is designed according to the TG–LSTM structure to realize the
synchronization of input filling and output prediction. Furthermore, the learning algorithm of TG–LSTM neural network is
established to uniformly train input filling and output prediction tasks. Finally, the experimental results of the Mackey-glass
benchmark data set, monthly average temperature data set and the ammonia nitrogen concentration prediction of waste
water treatment process show that compared with the traditional methods, the incomplete time series can be filled and
predicted by the TG–LSTM neural network model with higher accuracy. |
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