.png){kind=icon}
返回
Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conengprac.2019.104198.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Probabilistic latent variable regression models have recently caught much attention in the process industry, particularly for soft sensor applications. One of the main challenges for those models is how to effectively extract nonlinear features for latent variable regression. This paper proposes a nonlinear probabilistic latent variable regression (NPLVR) model based on the features extracted by variational auto-encoder. To extend the NPLVR model from shallow to deep structure, a hierarchical form of NPLVR model is proposed to extract deeper nonlinear information by stacking VAE. Under the same modeling framework, a semi-supervised version of hierarchical NPLVR model is further developed to handle the problem of scarce amount of labeled data samples, which is quite common in practical applications. Two industrial case studies are provided to demonstrate the effectiveness and superiority of the developed models.
Keyword:
Probabilistic latent variable regression model
Soft sensor
Nonlinear features
Variational auto-encoder
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:
4.6
论文数:
5.7K
被引数:
1.1W
机构
引用论文
Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers用于工业橡胶搅拌机质量预测的实时半监督软传感器
PREAI
分享收藏Data Mining and Analytics in the Process Industry: The Role of Machine Learning流程工业中的数据挖掘和分析: 机器学习的作用IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容