Using Dynamic Recurrent Neural Networks
نویسندگان
چکیده
Absbaet-In this note, the approximation capability of a class of discrete-time dynamic locurrent neural networks @RN"s) is studied. Analytieal lpsufts presented show that some of the states of sucb a D R " described by a set of dMerence equatbms may be used to approximate uniformly a ate-space trqjectmy pradufed by either a dismte-time nonlinear system or a cont i" fhnctkon on a closed disente-tbme interval. This a p p r o x i " pro", however, bas to be d c d out by an adaptive learning process. This E.lwBltity provides the potcntiol for applications such as klenti$catioa and adaptive eontrd. .
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