Nonlinear system identification using recurrent networks

The authors present empirical results on the application of neural networks to system identification and inverse system identification. Recurrent and feedforward network models were used to build an emulator of a simple nonlinear gantry crane system, and for the inverse dynamics of the system. The relevant data were artificially generated from the differential equations describing the system. The experimental results show that recurrent networks performed marginally better than feedforward networks in terms of the mean square errors, for the system identification problem, as well as for the inverse system identification problem