The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Review of the applications of neural networks in chemical process control — simulation and online implementation
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Citations
Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
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
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
Identification and control of dynamical systems using neural networks
Narendra, K. S., & Parthasarathy, K. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80202 · Cited by 8,014
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Generalized predictive control—Part I. The basic algorithm
Clarke, D. W., Mohtadi, C., & Tuffs, P. S. (1987) · Automatica · doi:10.1016/0005-1098(87)90087-2 · Cited by 3,799
Networks for approximation and learning
Poggio, T., & Girosi, F. (1990) · Proceedings of the IEEE · doi:10.1109/5.58326 · Cited by 3,252
Gray, R. M. (1990) · Readings in Speech Recognition · doi:10.1016/b978-0-08-051584-7.50011-5 · Cited by 2,845
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Widrow, B., & Lehr, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58323 · Cited by 2,242
Cited by
From experiment design to closed-loop control
Hjalmarsson, H. (2005) · Automatica · doi:10.1016/j.automatica.2004.11.021 · Cited by 506
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Dobbelaere, M. R., Plehiers, P. P., Van de Vijver, R., Stevens, C. V., & Van Geem, K. M. (2021) · Engineering · doi:10.1016/j.eng.2021.03.019 · Cited by 323
Zhu, L.-T., Chen, X.-Z., Ouyang, B., Yan, W.-C., Lei, H., Chen, Z., & Luo, Z.-H. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c01036 · Cited by 294
Selvarajoo, A., Muhammad, D., & Arumugasamy, S. K. (2020) · Modeling Earth Systems and Environment · doi:10.1007/s40808-019-00663-2 · Cited by 54
Metadata
- Authors
- Azlan Hussain, M.
- Venue
- Artificial Intelligence in Engineering
- Publisher
- Elsevier BV
- Published
- 1999-01
- Volume
- 13
- Issue
- 1
- Pages
- 55-68
- ISSN
- 0954-1810 (ISSN-L)
- OpenAlex
- W2132267451
- Cited by
- 381
- References
- 47
- FWCI
- 20.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Azlan Hussain, M. (1999). Review of the applications of neural networks in chemical process control — simulation and online implementation. Artificial Intelligence in Engineering. https://doi.org/10.1016/s0954-1810(98)00011-9
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.
