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Neural networks for control and system identification
Abstract
A review is presented of the field of neuroengineering as a whole, highlighting the importance of neurocontrol and neuroidentification. Then a description is given of the five major architectures in use today in neurocontrol (in robotics, in particular) and a few areas for future research. The author concludes with comments on neuroidentification.>
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Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
Al-Tamimi, A., Lewis, F. L., & Abu-Khalaf, M. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.926614 · Cited by 1,075
Jiang, Y., & Jiang, Z.-P. (2012) · Automatica · doi:10.1016/j.automatica.2012.06.096 · Cited by 1,072
Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness
De Persis, C., & Tesi, P. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2019.2959924 · Cited by 983
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2773458 · Cited by 891
Metadata
- Authors
- Werbos, P. J.
- Venue
- Proceedings of the 28th IEEE Conference on Decision and Control
- Publisher
- IEEE
- Published
- 2003-01-07
- Pages
- 260-265
- ISSN
- 0191-2216 (ISSN-L)
- OpenAlex
- W1907796993
- Cited by
- 319
- References
- 0
- FWCI
- 2.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Werbos, P. J. (2003). Neural networks for control and system identification. Proceedings of the 28th IEEE Conference on Decision and Control. https://doi.org/10.1109/cdc.1989.70114
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