Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 109-115.DOI: 10.3778/j.issn.1002-8331.2101-0461

Previous Articles     Next Articles

Improved Mechanism of Prediction-Oriented Long Short-Term Memory Neural Network

WU Minghui, HOU Lingyan, WANG Chao   

  1. 1.Computer Open Systems Laboratory, Beijing Information Science and Technology University, Beijing 100101, China
    2.Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing 100101, China
  • Online:2021-11-01 Published:2021-11-04



  1. 1.北京信息科技大学 计算机开放系统实验室,北京 100101
    2.北京材料基因工程高精尖中心,北京 100101


Based on time series data modeling, Long Short-Term Memory neural network(LSTM) can be used to solve prediction-oriented problems. In real scenarios, the prediction accuracy of LSTM is often related to the length of the input sequence, and valid historical information will be overwhelmed by the newly input data. To solve this problem, it is proposed to construct a reinforcement gate in the LSTM node to extract the forgotten information, and to select, merge, and enter the memory unit in proportion to the memory information to increase the gradient conduction ability in the learning process, so that the network can deal with relatively distant information, stay sensitive to improve memory ability. The experiment uses industrial fault data. When the sequence length exceeds 100, the prediction error of the improved model with enhanced gate mechanism is lower than other LSTM models. The gap in prediction accuracy increases with the increase of the sequence. When the sequence length increases to 200, the prediction error(RMSE/MAE) of the improved model is reduced by 26.98% and 35.85% respectively compared with the original model.

Key words: long short-term memory neural network, time series prediction model, memory enhaunce mechanism, deep learning



关键词: 长短时神经网络, 时间序列预测模型, 记忆增强机制, 深度学习