  Nonlinear system identification remains an important open challenge across
research and academia. Large numbers of novel approaches are seen published
each year, each presenting improvements or extensions to existing methods. It
is natural, therefore, to consider how one might choose between these competing
models. Benchmark datasets provide one clear way to approach this question.
However, to make meaningful inference based on benchmark performance it is
important to understand how well a new method performs comparatively to results
available with well-established methods. This paper presents a set of ten
baseline techniques and their relative performances on five popular benchmarks.
The aim of this contribution is to stimulate thought and discussion regarding
objective comparison of identification methodologies.
