Causal Graph Spatial--Temporal Autoencoder for Reliable and Interpretable Process Monitoring.
- DOI
- 10.1109/tnnls.2026.3656591
- Published
- 2026 Aug
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- 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 = {Zhang X and Song C and Dai W and Zhang Z and Gao K and Gao F},
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 - Zhang X AU - Song C AU - Dai W AU - Zhang Z AU - Gao K AU - Gao F 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
X, Z., C, S., W, D., Z, Z., K, G., & F, G. (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
- pubmed · retrieved 2026-11-10T12:04:19.018Z