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Updated: May 6, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Published on: October 28, 2022

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图形补丁变压器:具有适应式图形学习的补丁交互变压器,用于多变量时间序列预测

Chunyi Hou1, Yongchuan Yu1, Jinquan Ji1

  • 1School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China.

Neural networks : the official journal of the International Neural Network Society
|October 5, 2025
PubMed
概括

本研究介绍了Graph-Patchformer,这是一种用于多变量时间序列 (MTS) 预测的新型深度学习框架. 它有效地捕捉了系列内部和系列间的依赖性,优于对基准数据集的现有方法.

关键词:
深度学习是一种深度学习.图表学习学习图表学习信息利用瓶信息利用瓶多头自我注意力机制.多变量时间序列预测.

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