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Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks

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3 Author(s)
Liang Jin ; Coll. of Eng., Saskatchewan Univ., Saskatoon, Sask., Canada ; P. N. Nikiforuk ; M. M. Gupta

In this note, the approximation capability of a class of discrete-time dynamic recurrent neural networks (DRNN's) is studied. Analytical results presented show that some of the states of such a DRNN described by a set of difference equations may be used to approximate uniformly a state-space trajectory produced by either a discrete-time nonlinear system or a continuous function on a closed discrete-time interval. This approximation process, however, has to be carried out by an adaptive learning process. This capability provides the potential for applications such as identification and adaptive control

Published in:

IEEE Transactions on Automatic Control  (Volume:40 ,  Issue: 7 )