摘要：AbstractIn data-based modelling communities, such as system identification, machine learning, signal processing and statistics, benchmarks are essential for testing and comparing old and new techniques for the estimation of models. During the last years, it has become customary in system identification to rely on data sets built from randomly generated systems. In this article we discuss the implications of this practice, in particular when using data sets generated with the MATLABr command drss, and advocate the cautious use of comparisons based on these benchmarks.
