  Neural networks are known to be effective function approximators. Recently,
deep neural networks have proven to be very effective in pattern recognition,
classification tasks and human-level control to model highly nonlinear
realworld systems. This paper investigates the effectiveness of deep neural
networks in the modeling of dynamical systems with complex behavior. Three deep
neural network structures are trained on sequential data, and we investigate
the effectiveness of these networks in modeling associated characteristics of
the underlying dynamical systems. We carry out similar evaluations on select
publicly available system identification datasets. We demonstrate that deep
neural networks are effective model estimators from input-output data
