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- W1992023300 abstract "ABSTRACT In this paper we discuss the means by which recurrent connections are used in neural control system architectures. Wefirst consider the state feedback approach to control and the role of recurrent neural networks for plant modeling andcontrol. 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 ifiustration, we applydynamic 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. 1 Introduction The application of &tIfiCIal neural networks to problems in the control of nonlinear dynamical y8tems has beendominated largely by static neural architectures trained by gradient descent. These neural control architectures canroughly be categorized as belonging to one of two classee. The most common architecture considered is the multilayerperceptron architecture (or simply MLP network), consisting oflayers of nodes arranged in a feedforward fashion, suchthat the outputs of nod in a layer form the inputs to nodes in succeeding layers. These architectures are consideredto encode global" @default.
- W1992023300 created "2016-06-24" @default.
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- W1992023300 date "1993-08-19" @default.
- W1992023300 modified "2023-09-23" @default.
- W1992023300 title "<title>Roles of recurrence in neural control architectures</title>" @default.
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- W1992023300 doi "https://doi.org/10.1117/12.152616" @default.
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