Feature-aligned Stacked Autoencoder: A Novel Semi-supervised Deep Learning Model for Pattern Classification of Industrial Faults-Reference-Cited by-同舟云学术

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Feature-aligned Stacked Autoencoder: A Novel Semi-supervised Deep Learning Model for Pattern Classification of Industrial Faults

Published:2021 Issue: Volume: Page:1-1
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Zhang Xinmin,Zhang Hongyi,Song Zhihuan

Funder

National Natural Science Foundation of China

State Key Laboratory of Industrial Control Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/9184921/09648028.pdf?arnumber=9648028

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Dual‐noise autoencoder combining pseudo‐labels and consistency regularization for process fault classification;The Canadian Journal of Chemical Engineering;2024-09-03

2. Quadratic Neuron-Empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection;IEEE Transactions on Artificial Intelligence;2024-09

3. A Novel Applicable Shadow Resistant Neural Network Model for High-Efficiency Grid-Level Pavement Crack Detection;IEEE Transactions on Artificial Intelligence;2024-09

4. Industrial process fault diagnosis based on domain adaptive broad echo network;Journal of the Taiwan Institute of Chemical Engineers;2024-06

5. Nonlinear Dynamic System Based on SAE-LDS Model for Fault Diagnosis;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

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