A critique of neural networks for discrete-time linear control

Full text not archived in this repository.

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Warwick, K. (1995) A critique of neural networks for discrete-time linear control. International Journal of Control, 61 (6). pp. 1253-1264. ISSN 0020-7179 doi: 10.1080/00207179508921955

Abstract/Summary

This paper discusses the use of multi-layer perceptron networks for linear or linearizable, adaptive feedback.control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parametrization. A comparison is made with standard, non-perceptron algorithms, e.g. self-tuning control, and it is shown how gross over-parametrization can occur in the neural network case. Because of the resultant heavy computational burden and poor controller convergence, a strong case is made against the use of neural networks for discrete-time linear control.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/17884
Identification Number/DOI 10.1080/00207179508921955
Refereed Yes
Divisions Science
Uncontrolled Keywords discrete time, linear system, control system, neural network, adaptive control, feedback system, method study, ystem description, parametrization
Publisher Taylor & Francis
Download/View statistics View download statistics for this item

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar