  Recent developments within deep learning are relevant for nonlinear system
identification problems. In this paper, we establish connections between the
deep learning and the system identification communities. It has recently been
shown that convolutional architectures are at least as capable as recurrent
architectures when it comes to sequence modeling tasks. Inspired by these
results we explore the explicit relationships between the recently proposed
temporal convolutional network (TCN) and two classic system identification
model structures; Volterra series and block-oriented models. We end the paper
with an experimental study where we provide results on two real-world problems,
the well-known Silverbox dataset and a newer dataset originating from ground
vibration experiments on an F-16 fighter aircraft.
