Causal Graph Spatial--Temporal Autoencoder for Reliable and Interpretable Process Monitoring
- DOI
- 10.1109/tnnls.2026.3656591
- Published
- 2026-08
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2026.3656591,
title = {Causal Graph Spatial--Temporal Autoencoder for Reliable and Interpretable Process Monitoring},
author = {Xiangrui Zhang and Chunyue Song and Wei Dai and Zheng Zhang and Kaihua Gao and Furong Gao},
year = {2026},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2026.3656591},
url = {https://doi.org/10.1109/tnnls.2026.3656591}
}RIS
TY - JOUR TI - Causal Graph Spatial--Temporal Autoencoder for Reliable and Interpretable Process Monitoring AU - Xiangrui Zhang AU - Chunyue Song AU - Wei Dai AU - Zheng Zhang AU - Kaihua Gao AU - Furong Gao PY - 2026 JO - IEEE Transactions on Neural Networks and Learning Systems DO - 10.1109/tnnls.2026.3656591 UR - https://doi.org/10.1109/tnnls.2026.3656591 ER -
APA
Zhang, X., Song, C., Dai, W., Zhang, Z., Gao, K., & Gao, F. (2026). Causal Graph Spatial--Temporal Autoencoder for Reliable and Interpretable Process Monitoring. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2026.3656591
Source records
- crossref · retrieved 2026-09-26T02:40:06.211Z