Inverting recurrent neural networks for internal model control of nonlinear systemsKambhampati, C., Craddock, R., Tham, M. and Warwick, K. (1998) Inverting recurrent neural networks for internal model control of nonlinear systems. In: Proceedings of the 1998 American Control Conference. ACC. IEEE, pp. 975-979. ISBN 0-7803-4530-4 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/ACC.1998.703554 Abstract/SummaryIn this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.
Altmetric Related URLs Deposit Details University Staff: Request a correction | Centaur Editors: Update this record |
Lists
Lists
