  Neural network models become increasingly popular as dynamic modeling tools
in the control community. They have many appealing features including nonlinear
structures, being able to approximate any functions. While most researchers
hold optimistic attitudes towards such models, this paper questions the
capability of (deep) neural networks for the modeling of dynamic systems using
input-output data. For the identification of linear time-invariant (LTI)
dynamic systems, two representative neural network models, Long Short-Term
Memory (LSTM) and Cascade Foward Neural Network (CFNN) are compared to the
standard Prediction Error Method (PEM) of system identification. In the
comparison, four essential aspects of system identification are considered,
then several possible defects and neglected issues of neural network based
modeling are pointed out. Detailed simulation studies are performed to verify
these defects: for the LTI system, both LSTM and CFNN fail to deliver
consistent models even in noise-free cases; and they give worse results than
PEM in noisy cases.
