The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A deep belief network based fault diagnosis model for complex chemical processes
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Citations
Cites
1 retracted
Zhang, Y. (2008) · Industrial & Engineering Chemistry Research · doi:10.1021/ie071496x · Cited by 96
RetractedLeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
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
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“So we get: ℎ = + ∈ = line( ) (A.5) = + ℎ ∈ = line( ) (A.6) 〈 ℎ 〉 = ℎ (A.7) The Contrastive Divergence is introduced to solve the calculation of 〈 〉 , and it works well enough to achieve success in many significant applications (Hinton, 2002).”
BackgroundGreedy 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
A plant-wide industrial process control problem
Downs, J. J., & Vogel, E. F. (1993) · Computers & Chemical Engineering · doi:10.1016/0098-1354(93)80018-i · Cited by 3,348
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852
A review of process fault detection and diagnosis
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00160-6 · Cited by 2,723
Cited by
Deep convolutional neural network model based chemical process fault diagnosis
Wu, H., & Zhao, J. (2018) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.04.009 · Cited by 517
Self-citationJalayer, M., Orsenigo, C., & Vercellis, C. (2021) · Computers in Industry · doi:10.1016/j.compind.2020.103378 · Cited by 382
Contrastive learning based self-supervised time-series analysis
Pöppelbaum, J., Chadha, G. S., & Schwung, A. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108397 · Cited by 131
Metadata
- Authors
- Zhang, Z., & Zhao, J.
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 107
- Pages
- 395-407
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2589808763
- Cited by
- 309
- References
- 40
- FWCI
- 20.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A deep belief network based fault diagnosis model for complex chemical processes. https://app.cochat.ai/papers/10.1016/j.compchemeng.2017.02.041
- The paper
- Zhang, Z., & Zhao, J. (2017). A deep belief network based fault diagnosis model for complex chemical processes. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2017.02.041
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.
