The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Data-driven soft sensor development based on deep learning technique
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 11,934
Partial least-squares regression: a tutorial
Geladi, P., & Kowalski, B. R. (1986) · Analytica Chimica Acta · doi:10.1016/0003-2670(86)80028-9 · Cited by 6,992
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 6,938
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Cited by
Advances and opportunities in machine learning for process data analytics
Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351
Machine learning based soft sensor model for BOD estimation using intelligence at edge
Pattnaik, B. S., Pattanayak, A. S., Udgata, S. K., & Panda, A. K. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00259-9 · Cited by 61
Deep learning controller for nonlinear system based on Lyapunov stability criterion
Zaki, A. M., El-Nagar, A. M., El-Bardini, M., & Soliman, F. A. S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05077-1 · Cited by 29
Du, X., Zhou, C., Tian, Y.-C., & Wang, K. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3384275 · Cited by 13
Metadata
- Authors
- Shang, C., Yang, F., Huang, D., & Lyu, W.
- Venue
- Journal of Process Control
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 24
- Issue
- 3
- Pages
- 223-233
- ISSN
- 0959-1524 (ISSN-L)
- OpenAlex
- W2085862958
- Cited by
- 549
- References
- 33
- FWCI
- 24.57
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Data-driven soft sensor development based on deep learning technique. https://app.cochat.ai/papers/10.1016/j.jprocont.2014.01.012
- The paper
- Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control. https://doi.org/10.1016/j.jprocont.2014.01.012
Data sources
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.
