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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 ; Nikiforuk, Peter N. ; Gupta, M.M.

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

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