The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Applications of artificial neural networks in chemical engineering
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Citations
Cites
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Introduction to the theory of neural computation
(1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,401
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
Reed, R. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.248452 · Cited by 1,708
What Size Net Gives Valid Generalization?
Baum, E. B., & Haussler, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.1.151 · Cited by 1,541
Cited by
A survey towards an integration of big data analytics to big insights for value-creation
Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494
Cloud manufacturing as a sustainable process manufacturing route
Fisher, O., Watson, N., Porcu, L., Bacon, D., Rigley, M., & Gomes, R. L. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.03.005 · Cited by 282
Rizkin, B. A., Popovich, K., & Hartman, R. L. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2018.11.016 · Cited by 40
Supervised machine learning for prediction of zirconocene-catalyzed α-olefin polymerization
Rizkin, B. A., & Hartman, R. L. (2019) · Chemical Engineering Science · doi:10.1016/j.ces.2019.115224 · Cited by 35
Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires
Conant, G., Ahadian, S., Zhao, Y., & Radisic, M. (2017) · Scientific Reports · doi:10.1038/s41598-017-12048-5 · Cited by 25
Metadata
- Authors
- Himmelblau, D. M.
- Venue
- Korean Journal of Chemical Engineering
- Published
- 2000-07
- Volume
- 17
- Issue
- 4
- Pages
- 373-392
- ISSN
- 0256-1115 (ISSN-L)
- 1975-7220
- OpenAlex
- W2041115671
- Cited by
- 273
- References
- 58
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Himmelblau, D. M. (2000). Applications of artificial neural networks in chemical engineering. Korean Journal of Chemical Engineering. https://doi.org/10.1007/bf02706848
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.