Transformer-based sequence-to-sequence soft sensor using missing data in industrial processes

doi:10.1016/j.chemolab.2026.105631

2026-01-09

PRE

DOI：10.1016/j.chemolab.2026.105631

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摘要

En 中文

• 引入自注意力机制以编码长期时间依赖关系。 • 使用长短期记忆网络（LSTM）来建模局部非线性并插补缺失数据。 • 端到端训练插补与预测，以确保完成的数据真实可信。

期刊

Chemometrics and Intelligent Laboratory Systems

IF:

3.8

论文数:

4.6K

被引数:

1.2W

机构

Zhejiang University of Technology

学者数:

3.2K

论文数: 1.1K

被引数: 3.0W

Zhejiang Key Laboratory of Digital Precision Measurement Technology Research

学者数:

论文数: 3

被引数: 0

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