The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Non-linear system identification using neural networks
Abstract
Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems. This paper investigates the identification of discrete-time nonlinear systems using neural networks with a single hidden layer. New parameter estimation algorithms are derived for the neural network model based on a prediction error formulation and the application to both simulated and real data is included to demonstrate the effectiveness of the neural network approach.
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Citations
Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Cited by
Inferring biological networks by sparse identification of nonlinear dynamics
Mangan, N. M., Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · IEEE Transactions on Molecular, Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2016.2633265 · Cited by 436
Neural networks for nonlinear internal model control
Hunt, K. J., & Sbarbaro, D. (1991) · IEE Proceedings D Control Theory and Applications · doi:10.1049/ip-d.1991.0059 · Cited by 367
Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Mrabet Bellaaj, N. (2018) · Energies · doi:10.3390/en11030620 · Cited by 336
(1997) · Elsevier eBooks · doi:10.1016/b978-0-12-526430-3.x5000-4 · Cited by 290
Metadata
- Authors
- CHEN, S., BILLINGS, S. A., & GRANT, P. M.
- Venue
- International Journal of Control
- Publisher
- Informa UK Limited
- Published
- 1990-01
- Volume
- 51
- Issue
- 6
- Pages
- 1191-1214
- ISSN
- 0020-7179 (ISSN-L)
- 1366-5820
- OpenAlex
- W2098398123
- Cited by
- 1,043
- References
- 1
- FWCI
- 54.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- CHEN, S., BILLINGS, S. A., & GRANT, P. M. (1990). Non-linear system identification using neural networks. International Journal of Control. https://doi.org/10.1080/00207179008934126
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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.
