  This paper introduces a network architecture, called dynoNet, utilizing
linear dynamical operators as elementary building blocks. Owing to the
dynamical nature of these blocks, dynoNet networks are tailored for sequence
modeling and system identification purposes. The back-propagation behavior of
the linear dynamical operator with respect to both its parameters and its input
sequence is defined. This enables end-to-end training of structured networks
containing linear dynamical operators and other differentiable units,
exploiting existing deep learning software. Examples show the effectiveness of
the proposed approach on well-known system identification benchmarks.
  Examples show the effectiveness of the proposed approach against well-known
system identification benchmarks.
