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Proceedings Paper

Roles of recurrence in neural control architectures
Author(s): Gintaras V. Puskorius; Lee A. Feldkamp
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Paper Abstract

In this paper we discuss the means by which recurrent connections are used in neural control system architectures. We first consider the state feedback approach to control and the role of recurrent neural networks for plant modeling and control. In this content, we provide an explicit formation for the computation of dynamic derivatives in recurrent neural network architectures as required for training by the dynamic gradient method. For illustration, we apply dynamic gradient methods to train recurrent neural network controllers for a series of cart-pole problems with the simultaneous objectives of pole balancing and cart centering.

Paper Details

Date Published: 2 September 1993
PDF: 16 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152522
Show Author Affiliations
Gintaras V. Puskorius, Ford Motor Co. (United States)
Lee A. Feldkamp, Ford Motor Co. (United States)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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