Attention-based recurrent neural network for multistep-ahead prediction of process performance
Attention-based recurrent neural network for multistep-ahead prediction of process performance
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DOI：
10.1016/j.compchemeng.2020.106931
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发表时间：
2020-09-02
影响因子：
4.3
通讯作者：
Huang, Yinlun
中科院分区：
文献类型：
--
作者：
Aliabadi, Majid Moradi;Emami, Hajar;Huang, Yinlun
基于注意力的RNN建模技术可用于研究各种需要多步预测的过程工程问题。这种类型的模型由一个将时间序列数据序列编码为新的表示形式的RNN，另一个将表示解码为目标序列的RNN，以及在两者之间添加的注意力模型组成，该模型允许模型专注于对预测目标序列至关重要的输入序列的一部分。具有这种用于高级表示的深层架构的模型可以学习非常复杂的动态系统。为了证明建模方法的有效性，催化剂活性预测问题的比较研究。(C)2020由Elsevier Ltd.出版
Attention-based RNN modeling technique could be potentially used for investigating a variety of process engineering problems that require multiple step predictions. This type of model consists of an RNN that encodes a sequence of time series data into a new representation form, an another RNN that decodes the representation into a target sequence, as well as an attention model added in between that allows the model to focus on part of the input sequence that are critical to predicting the target sequence. The model with this deep architecture for high-level representations can learn very complex dynamic systems. To demonstrate the effectiveness of the modeling approach, a comparative study on the problem of catalyst activity prediction is illustrated. (C) 2020 Published by Elsevier Ltd.