  The application of neural networks to non-linear dynamic system
identification tasks has a long history, which consists mostly of
autoregressive approaches. Autoregression, the usage of the model outputs of
previous time steps, is a method of transferring a system state between time
steps, which is not necessary for modeling dynamic systems with modern neural
network structures, such as gated recurrent units (GRUs) and Temporal
Convolutional Networks (TCNs). We compare the accuracy and execution
performance of autoregressive and non-autoregressive implementations of a GRU
and TCN on the simulation task of three publicly available system
identification benchmarks. Our results show, that the non-autoregressive neural
networks are significantly faster and at least as accurate as their
autoregressive counterparts. Comparisons with other state-of-the-art black-box
system identification methods show, that our implementation of the
non-autoregressive GRU is the best performing neural network-based system
identification method, and in the benchmarks without extrapolation, the best
performing black-box method.
