The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Neural networks for control systems—A survey
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Citations
Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
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 logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
The perceptron: A probabilistic model for information storage and organization in the brain.
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On the approximate realization of continuous mappings by neural networks
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Cited by
Neural Networks: A Review from a Statistical Perspective
Cheng, B., & Titterington, D. M. (1994) · Statistical Science · doi:10.1214/ss/1177010638 · Cited by 1,048
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452
“neural networks have been used for control for decades [12], [33], limitations on computation and algorithms have made large neural network policies very difficult to learn.”
MethodMiglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
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Nagy, Z. K. (2007) · Chemical Engineering Journal · doi:10.1016/j.cej.2006.10.015 · Cited by 211
Metadata
- Authors
- Hunt, K. J., Sbarbaro, D., Żbikowski, R., & Gawthrop, P. J.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 1992-11
- Volume
- 28
- Issue
- 6
- Pages
- 1083-1112
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W1969705022
- Cited by
- 1,864
- References
- 15
- FWCI
- 55.89
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Neural networks for control systems—A survey. https://app.cochat.ai/papers/10.1016/0005-1098(92)90053-i
- The paper
- Hunt, K. J., Sbarbaro, D., Żbikowski, R., & Gawthrop, P. J. (1992). Neural networks for control systems—A survey. Automatica. https://doi.org/10.1016/0005-1098(92)90053-i
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.