.png){kind=icon}
返回
Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application
.png){kind=icon}
OA
AI
DOI：10.1021/acsomega.2c07400.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Soft sensors are mathematical methods that describe the dependence of primary variables on secondary variables. A nonlinear characteristic commonly appears in modern industrial process data with increasing complexity and dynamics, which has brought challenges to soft sensor modeling. To solve these issues, a novel supervised attention-based bidirectional long short-term memory (SA-BiLSTM) is first proposed in this paper to handle the nonlinear industrial process modeling with dynamic features. In this SA-BiLSTM model, an attention mechanism is introduced to calculate the correlation between hidden features in each time step, thus avoiding the loss of important information. Furthermore, this approach combines historical quality information and a moving window through a supervised strategy of quality variables. Such manipulation not only extracts and exploits nonlinear dynamic latent information from the process and quality variables but also enhances the model's learning efficiency and overall prediction performance. Finally, two real industrial examples demonstrate the superiority of the proposed method compared to conventional methods.
Keyword:
BIG PROCESS DATA
PREDICTION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
3.4W
被引数:
9.8W
机构
引用论文
.png){kind=icon}
Mixture robust semi-supervised probabilistic principal component regression with missing input data
PREAI
分享收藏分享收藏Rethinking the Value of Just-in-Time Learning in the Era of Industrial Big Data工业大数据时代及时学习价值的再思考
PREAI
分享收藏
查看更多内容