The DOI matches its Crossref record; 209 of the 209 references it cites were found in a registry or an index.
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
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Items 201–209 of 209
Fault detection with improved principal component pursuit method
Pan, Y., Yang, C., An, R., & Sun, Y. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.07.003 · Cited by 22
Reinforced Robust Principal Component Pursuit
Brahma, P. P., She, Y., Li, S., Li, J., & Wu, D. O. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2671849 · Cited by 22
Similar Batch Process Monitoring With Orthogonal Subspace Alignment
Zhu, J., & Gao, F. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2803785 · Cited by 19
Self-citationNing, K., Liu, M., & Dong, M. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.07.045 · Cited by 18
A comparison of different procedures for principal component analysis in the presence of outliers
Alkan, B. B., Atakan, C., & Alkan, N. (2015) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1005063 · Cited by 14
Zhu, J., & Gao, F. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b03590 · Cited by 13
Self-citationLearning Continuous Time Bayesian Network Classifiers Using MapReduce
Rossetti, M., & Villa, S. (2014) · Journal of Statistical Software · doi:10.18637/jss.v062.i03 · Cited by 11
Neural network methods in analysing and modelling time varying processes
Koskela, T. (2003) · Aaltodoc (Aalto University) · Cited by 10
Real-Time Optimal State Estimation Scheme With Delayed and Periodic Measurements
Lee, H., Kang, S., & Han, S. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774731 · Cited by 8
Cited by
Greenacre, M., Groenen, P. J. F., Hastie, T., D’Enza, A. I., Markos, A., & Tuzhilina, E. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00184-w · Cited by 1,773
A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes
Jiang, Y., Yin, S., Dong, J., & Kaynak, O. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3033153 · Cited by 667
Parallel spatio-temporal attention-based TCN for multivariate time series prediction
Fan, J., Zhang, K., Huang, Y., Zhu, Y., & Chen, B. (2023) · Neural Computing and Applications · doi:10.1007/s00521-021-05958-z · Cited by 440
An incorrect data detection method for big data cleaning of machinery condition monitoring
Xu, X., Lei, Y., & Li, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2903774 · Cited by 134
Variable time delay estimation in continuous industrial processes
Cattaldo, M., Ferrer, A., & Måge, I. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105082 · Cited by 18
Metadata
- Authors
- Zhu, J., Ge, Z., Song, Z., & Gao, F.
- Venue
- Annual Reviews in Control
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 46
- Pages
- 107-133
- ISSN
- 1367-5788 (ISSN-L)
- OpenAlex
- W2895250764
- Cited by
- 308
- References
- 209
- FWCI
- 18.15
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
