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Nonlinear dynamical systems control using a new RNN temporal learning strategy
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DOI：10.1109/TCSII.2005.852191.png)
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The ability of recurrent neural networks (RNN) to handle time-varying input/output through its own temporal operation is discussed. A new class of continuous-time (CT) RNN is proposed and it is proved that any finite time trajectory of a given n-dimensional dynamical CT system with input can be approximated by the internal state of the output units of an RNN. The proposed RNNs are extended for temporal processing.
Keyword:
continuos-time recurrent neural networks (RNNs)
temporal processing
two-dimensional (2-D) system theory
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