  Deep state space models (SSMs) are an actively researched model class for
temporal models developed in the deep learning community which have a close
connection to classic SSMs. The use of deep SSMs as a black-box identification
model can describe a wide range of dynamics due to the flexibility of deep
neural networks. Additionally, the probabilistic nature of the model class
allows the uncertainty of the system to be modelled. In this work a deep SSM
class and its parameter learning algorithm are explained in an effort to extend
the toolbox of nonlinear identification methods with a deep learning based
method. Six recent deep SSMs are evaluated in a first unified implementation on
nonlinear system identification benchmarks.
