Roles of recurrence in neural control architectures

Gintaras V. Puskorius; Lee A. Feldkamp

doi:10.1117/12.152616

19 August 1993 Roles of recurrence in neural control architectures

Gintaras V. Puskorius, Lee A. Feldkamp

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152616
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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 context, we provide an explicit formulation 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.

Citation Download Citation

Gintaras V. Puskorius and Lee A. Feldkamp "Roles of recurrence in neural control architectures", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152616

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