Nonlinear dynamic system identification with dynamic recurrent neural networks

We work with the dynamical recurrent neural network as a tool for system identification. We train this network using a time-dependent back-propagation learning algorithm and we show that for modeling a nonlinear dynamical system, our neural device has good performance for interpolation and extrapolation, and is very robust in the presence of noise.